{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# week2 work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "\n",
    "# 画图\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 数据观察"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(764, 9)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 764 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 764 non-null int64\n",
      "Glucose                     764 non-null float64\n",
      "BloodPressure               764 non-null float64\n",
      "SkinThickness               764 non-null float64\n",
      "Insulin                     764 non-null float64\n",
      "BMI                         764 non-null float64\n",
      "DiabetesPedigreeFunction    764 non-null float64\n",
      "Age                         764 non-null int64\n",
      "Outcome                     764 non-null int64\n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 59.7 KB\n"
     ]
    }
   ],
   "source": [
    "train.info() # 观察数据情况，是否有缺失项等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>764.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.786649</td>\n",
       "      <td>121.541885</td>\n",
       "      <td>72.396597</td>\n",
       "      <td>29.092932</td>\n",
       "      <td>140.710733</td>\n",
       "      <td>32.431152</td>\n",
       "      <td>0.472260</td>\n",
       "      <td>33.187173</td>\n",
       "      <td>0.345550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.278714</td>\n",
       "      <td>30.405250</td>\n",
       "      <td>12.120454</td>\n",
       "      <td>8.801763</td>\n",
       "      <td>86.580617</td>\n",
       "      <td>6.882536</td>\n",
       "      <td>0.331619</td>\n",
       "      <td>11.764745</td>\n",
       "      <td>0.475859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.244000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   764.000000  764.000000     764.000000     764.000000  764.000000   \n",
       "mean      3.786649  121.541885      72.396597      29.092932  140.710733   \n",
       "std       3.278714   30.405250      12.120454       8.801763   86.580617   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.000000      64.000000      25.000000  122.000000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  140.000000      80.000000      32.000000  127.250000   \n",
       "max      13.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  764.000000                764.000000  764.000000  764.000000  \n",
       "mean    32.431152                  0.472260   33.187173    0.345550  \n",
       "std      6.882536                  0.331619   11.764745    0.475859  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.244000   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.500000                  0.626250   40.250000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`怀孕次数：应该不是五年内的，但是最大17次还是有些夸张，医学上有妊娠期糖尿病一说，零值可能是缺失\n",
    "葡萄糖浓度：两小时内葡萄糖是否回归正常值，如果两小时后还是很高，很可能糖尿病，相关性应该还是很大，有零值也很过分呀，应该是缺失\n",
    "舒张压：百度显示，高血压的患者更可能患糖尿病，此处零值也是缺失值。\n",
    "三头肌皮肤褶皱厚度：男性为12.5 mm，女性为16.5 mm。营养调查的必测项目。在相当于标准值的80%〜90%为 轻度营养不良，60%〜80%为中度营养不良，< 60%为重度营养不良。到25%都为0，应该是缺失值。\n",
    "胰岛素：用来分泌葡萄糖的，少了估计糖尿病几率大\n",
    "BMI：胖的人更可能患糖尿病，有缺失\n",
    "家族病史：遗传性。\n",
    "年龄：医学上，年龄越大，患病率越高。`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 764 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 764 non-null int64\n",
      "Glucose                     764 non-null float64\n",
      "BloodPressure               764 non-null float64\n",
      "SkinThickness               764 non-null float64\n",
      "Insulin                     764 non-null float64\n",
      "BMI                         764 non-null float64\n",
      "DiabetesPedigreeFunction    764 non-null float64\n",
      "Age                         764 non-null int64\n",
      "Outcome                     764 non-null int64\n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 59.7 KB\n"
     ]
    }
   ],
   "source": [
    "# 将0值转换为缺失值\n",
    "train[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']]=train[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(0,np.NAN)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>764.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.786649</td>\n",
       "      <td>121.541885</td>\n",
       "      <td>72.396597</td>\n",
       "      <td>29.092932</td>\n",
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       "      <td>33.187173</td>\n",
       "      <td>0.345550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.278714</td>\n",
       "      <td>30.405250</td>\n",
       "      <td>12.120454</td>\n",
       "      <td>8.801763</td>\n",
       "      <td>86.580617</td>\n",
       "      <td>6.882536</td>\n",
       "      <td>0.331619</td>\n",
       "      <td>11.764745</td>\n",
       "      <td>0.475859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.244000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   764.000000  764.000000     764.000000     764.000000  764.000000   \n",
       "mean      3.786649  121.541885      72.396597      29.092932  140.710733   \n",
       "std       3.278714   30.405250      12.120454       8.801763   86.580617   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.000000      64.000000      25.000000  122.000000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  140.000000      80.000000      32.000000  127.250000   \n",
       "max      13.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  764.000000                764.000000  764.000000  764.000000  \n",
       "mean    32.431152                  0.472260   33.187173    0.345550  \n",
       "std      6.882536                  0.331619   11.764745    0.475859  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.244000   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.500000                  0.626250   40.250000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##怀孕次数为0属于正常的，因此不做填补，其他的使用中值填补。\n",
    "from sklearn.preprocessing import Imputer\n",
    "imp=Imputer(copy=False,axis=0,strategy='median')\n",
    "train[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']]=imp.fit_transform(train[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']])\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(764, 9)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 怀孕次数17太夸张，算成离群点剔除掉\n",
    "train = train[train.Pregnancies < 14]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(train.shape[0]), train[\"Pregnancies\"].values,color='red')\n",
    "plt.title(\"Distribution of Pregnancies\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 探索特征的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=train.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "train_corr = train.corr().abs()\n",
    "train_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6FBsdQ+FUQwECgwoTfyguXb0ajWrRqf/9jOkzkqSEpJTyXHlzMfibYUx890d2b9iZJTFfrajIQwSVOH+1uXhwMaIv+H9PXcfpdJIvX16Oxp8/TNvf14apGQwfq1LtJvycTjaHbMuk6K9eREQ0JUsEp7wuUSIoZVjYOZGp6rjzv4G4uKOAu5diwsQveKzPAEJD3XMEataqCpDyesrkWTRsWDfTc/knIsKjKFkybd6RFwxziwiPSulBcTqd5M+fj7i4eMIj0q8bFele95vxE2lwS2uaNe9EXPxR9uwJTdnWb1Pd+8iatRtJTk6mcOFCmZrjlYqIiKJkifMn2SVKFE835M+9n7jrOJ1O8qfaB87ZuXMvp06eomq1mwB3Q/bHnz7j54nTmD5tbiZnce0kHYrBL1Xvu1+xwiQdTvs9mHz0b2xiIgDHfplDjmoVU5a5jrjrJoZHc2r1JnJUKZ8FUV87MVGxFA4+n39gUGHiDqf/HajVuBZd+ndlZO8RaX4H/q/Z5Mx/eIEaMFfn3ByY2tbap621CZ7y1DOEDTA/Vb2q1trenvJLOZvquQvwM8Y0BVoAt3p6VDYAOT11Eu35yxEurvwGDQYYnSq+Ctbar621u4C6uBsyo40x6W7dYa390lpbz1pbr89DD17h28k/sWXDdkqXK0WJ0kH4+fvRpmNLFs3984rW9fP346PxbzH9l9nMm/FHJkeaefaE7CaobDBFSxXDz9+PRu1uZ838VWnqlK1WjidGP8WY3iM5Hnv+JN7P34+XvhzMksmLWDF7eVaH/q+FrN9C2XKlKVW6BP7+fnS4rw3z5yxKU2f+74u4/4EOANzToRXL/zz/mRhjaNuhFdOnpG/AdOzUhmkZlF9P1q0LoXyFMtx4Y0n8/f3p3Lkds2bNT1Nn1uz5dO/hviPdvffezZIlfwGQP38+pkz+huHD3mblyvNDSCIjo6lcpWLKCXqz5o3ZeZ1N3l6zdiMVKpSlTJlS+Pv707VLB2bOnJemzsyZ8+jZ834AOnW6h0WLl6eUd+3SgYCAAMqUKUWFCmVZvWYDQMrk/FKlgunYsQ0Tf54KwPTpc7nzzkYAVKxYjoCAAGJi0p8UesO6dZvS7QOzZ6W96cTs2QtS7QNtWLJkBQA33lgSp9MJQKlSJahYqRwH9ruHz4777C127tzDJx9/TXZyZvNO/G8Mxq9EMfD3I9/dTTi5aGWaOs4i5xufeZs1JGGfu7HuyJcX4+8exOEokI9cN1dNN/n/erc7ZBfBZYMp5vkduKPdHay+4HegXLVy9BvdnxG9R3AsNt01V/ExugtZ5lsJfGqMqWCt3WOMyQ2UBHYA5YwxZay1YcCVzDHJD8Rba08ZYyrj7t25nIXAk8CHxhgnkMdaezzV8rnACGPMj9baE8aYEkAi7n0jzlr7g2e+TK8rSzdrDRw+hjUbNnH06HGad+zBU7170umCSa/ZmcvlYtSgd/li4kc4nQ5+mzCTvTtD6ffSY2wN2cHiuX9SvXYVPvzmLfIVuIGmrRrTb+BjdGzSjdbtW1C3YR0KFMxPx673ADDkmRHs3Lr7Mu96fUl2JfOfYV8w9LvXcDgd/DFpAeG7D9J1QDf2btrD2gWr6Tm4Fzlz5+KFce7bAsdEHuGtPm9ya9vGVGlQjbwFbqBpZ/ck+E9f/IiwbaGXekuvc7lcvPrSKH789QscTic///gbu3bs5cVB/QjZsJX5cxYz8YcpfPT5aJatnc3R+GM81WdgyvoNb6tHVOShlJO21Np2vIuHul5fQ6cu5HK5eGHAMKZN/w6n08l3301i+/bdDH31edav38zsWQv4dvwk/vP1+2zavJj4+KM8/NDTADzR9yHKlb+RVwY9wyuD3NMH27frSXTUYUaN+oi58yaRlJjIgYMRPPH4i5cKI8u5XC6efW4os2b9hNPhYPy3P7Nt2y6GD3+RdetCmDlzPv/9ZiLjx49l+7ZlxMcfpXsP9//ltm27+OXXGWwKWUSSy8Uzzw4h2fP3Hyb9/BWFAguSlJjEM88M4ehR98ndN+Mn8p+v3mPDhoUkJiTyaO/nvJb7hdz7wHCmTv8Op9PB99/9ksE+8DP/+foDQjYvIj7+GL08+8Ctt9XnhRf6kpiURHJyMs8/9yqxsfHcems9unW/jy2bd/DXSved+l4b/g7z5i72YqZXyJXMkZHjKPmfN8Hh4PiUeSTs2U/g0z05s2U3JxetpGCPDuRp1hCSXLiO/U30oPcACChXimKvPwPJFhyGuK8mZbsGTLIrmc9f/ZzXv38Dh9PBgp/nc2DXAboP6M7uzbtZPX81jwx5lJy5c/LKZ68AcCTyCCN7jwBgzK9vUbJ8SXLmyck3q8YzduBYNixd782Uso6P/h0Yky3Gg1+njDEnrLV5LygrA8z0zBs5V9YMeAvI4Skaaq2dboxpB7wDxACrgWLW2u7GmNeAE9badz3rbwHaAlHAVKAEsBP3vJnXrLWLU8dijOkMtLXW9vJM5v8SKIe7Z+ZJa+2KC+o/C5z74wAngB5ABU9sybgbNE9aa9de7LNIjNn3f78j1anW7fKVfFTlnEUvX8nHrfx73+Ur+bD4M9lkLkEmSkhK9HYIXpXDL+DylXzc+htv8nYIXvXCSV0Xn3Fg5uVG2GSps1sXZvr5WY5qzbM8Z+1pV+HCxounLAyofkHZH0D9DDaxyFpb2bhnIn6Key4N1trXLlg/9fbaXC4Wa+2vwK+e54eADpep/xHw0QVV9uLunRERERGR7Eh/B0YywWOeif1bcQ8P+8LL8YiIiIiIXNfUA+NF1toPgA+8HYeIiIiI+CAfnQOjHhgREREREck21AMjIiIiIuKDrHV5O4RMoR4YERERERHJNtQDIyIiIiLii3QXMhEREREREe9SD4yIiIiIiC/SXchERERERES8Sz0wIiIiIiK+yEfnwKgBIyIiIiLii5J1G2URERERERGvUg+MiIiIiIgv8tEhZOqBERERERGRbEM9MCIiIiIivki3URYREREREfEu9cCIiIiIiPgizYERERERERHxLvXAiIiIiIj4Is2BERERERER8S71wIiIiIiI+CL1wIiIiIiIiHiXemBErpENW3/ydghe1a3u894Owatq5Cnl7RC86tEchbwdgtdNCTjp7RC8andCrLdD8Lr3Tt/g7RC8akiSy9shyAWs9c3/EzVg5JqoU62bt0Pwqv/3xouIiIhIVlEDRkRERETEF2kOjIiIiIiIiHepB0ZERERExBdZ9cCIiIiIiIh4lXpgRERERER8kebAiIiIiIiIeJd6YEREREREfJGPzoFRA0ZERERExBdpCJmIiIiIiIh3qQdGRERERMQX+egQMvXAiIiIiIhItqEeGBERERERX6Q5MCIiIiIiIt6lHhgREREREV+kHhgRERERERHvUg+MiIiIiIgv0l3IREREREREvEs9MCIiIiIivkhzYERERERERLxLPTAiIiIiIr5Ic2BERERERES8Sz0wct1rdGdDXhn5PE6ng8k/Tufrj79Ps7xuw9q8POJ5KlUtz8AnXmX+zEUA3FStIq++/RJ58+YhOTmZLz8cz5xpC7yRQqYZOup9li5fTaGCBZj6w+feDifT1G5Sh0eGP4bD6WDhxPlM/WxymuVt+7Sn+QOtcCW5OB53jHEDPyYm4ghlqpblsTf7kitvbpJdyUz55Bf+mrnMS1n8e3Wb1qXva31xOB3MmTCHX8b9kmZ59Vuq88TwJyhbpSxj+o1h2ezzOT466FHqN68PwISPJrB0xtIsjf1aKXZnTeq80RPjdLDvp8Xs/GRGhvVK3NOA2/7zLAtaDyU+JJSid1Sn5pAHcPj7kZyYRMgbP3Fk+basDf4aqNWkDg8N74PD6WDRxPlM/2xKmuV392nPnQ+0JDnJxfG443zhOQYKlyjC81+8gsPhwM/fydzxs1jw41wvZXF1br2zAS++8SwOp4OpP83k209+TLO8TsNavPDGM1SoUo4hfV9n4azFaZbnyZubX5b+wOLfl/L2kA+zMPJro1qT2nQZ9ggOp4NlPy9k7mdT0yxv0bstjR5oTnKSixNxx/n2pXHERcRQ6dZqdHm1V0q94uWD+erpDwmZtyaLM7g6+ZvWocyIRzEOB4cnLCDyk98yrFfonlup9NVANrceyMlNezH+fpR9uy95a5bHJlv2D/ua4yu2ZnH0XuSjc2DUgLnOGGOKAR8ADYF4IAF42/P8RWttWy+Gl+UcDgdDx7zIY12eITryMD/P/YZFc/9k366wlDpREYcY+uwIej3ZLc26Z06fYXD/NzgQepAixQozaf54li9ayd/HT2RxFpmn490t6dapPYNHvOvtUDKNw+Gg94gnGNF9OHHRsYye/i5rF6wmfPfBlDqhW0N5ue0AEs4k0KpHa3oO6sUH/d/h7OmzfPz8h0SHRVGwaCHemvUeG5du4NTxk17M6J9xOBz0G9mPwd0GExMVw0czP2LV/FUc2H0gpc7hiMO8N+A9Oj3RKc269ZvVp3z18vS7qx/+Af68/evbrF20llMnTmV1GlfHYbh5VC+Wdh3Nqag4Wvw+gsh56/l7V0Saan55clKxz13ErtuTUpYQ9zfLHnqXM4eOku+mktwx4WVm3vx0VmdwVYzDwSMjnmBU9+HERsfy5vR3WLdgNRG7w1PqhG3dx5C2L5BwJoEWPVrTbdDDjO3/LvGH4xl+38skJSSRI3dO3pk3lnXzVxN/ON6LGf1zDoeDl0cNoF/X5zkUdYTvfv+KpfOWE5rqtyA6/BCvPTuKnk8+kOE2+r7ch/UrNmZRxNeWcTh48I3efNhjBPHRcQyaPppN89cStef8PnBgWyhL2r1M4pkE7ujRik6DevJV/w/YtWIrI+8eCEDu/HkZueRjti0N8VYq/47DQdlRj7H9gddJiIql+uy3iZ+7htOpjgEAR56cFO99N3+v25VSVrR7CwA2NX8ev8D8VP5xKFvavATWZmkKcm1pCNl1xBhjgKnAUmttOWttXeABoKR3I/OeGjdX5UBoOOH7I0lKTOL3qfNp1vqONHUiD0axa9sekpPTfhnt33eQA6Huk9wjh2KIi4mnYGDBLIs9K9SrXYP8+W7wdhiZqkLtikSHRXP44CGSEpNYPuNP6rVskKbO1hWbSTiTAMCuDTspFBQIQFRoJNFhUQDEH47jWMwx8hXKl7UJXKVKtSsRGRZJ9IFokhKTWDJ9CQ1bNUxT53D4YcJ2hGEv+EEuXbE0m1dtJtmVzNnTZwndFkrdpnWzMvxrolCd8pwIO8TJA0ewiS4OTltJibvS51Ht5c7s/HQmrrMJKWVHt+znzKGjABzfGY4jhz+OgOx17c59DERx+OAhXIlJrJixjHotb0lTZ9uKLSnHwJ5Ux4ArMYmkhCQA/AP8MQ6TtcFfI9XqVOFgWAQRB6JISkxi3rSFNLmrcZo6UeHR7Nm+N91vAUDlmpUILFyIlUuyV6/DOWVrV+Dw/mhiDh7GlZjE2hnLqdWqXpo6u1ZsJdGzD4Ru2EWB4oXSbafu3Q3ZsnhDSr3sIm+dCpwJi+LsgUPYxCRipy2j4F0N0tUr9VI3IsdNxab6DshVqRTH/9wEQFLsMVzHTpKnVvksi93rbHLmP7xADZjrSzMgwVqbMhbIWrvfWvtx6krGmNeMMS+mer3FGFPG8/whY8wmY0yIMeZ7T9mNxpiFnvKFxpjSnvL7PeuGGGOWesqcxph3jDFrPPWfyPSsL6Fo8SJERx5OeX0o8jBFixf5x9upXqcq/v7+HAwLv3xlua4UKh5IbFRMyuu4qFgCiwdetH7zri3ZsHhduvIKtSriF+DHof3RmRJnZilcvDBHIo+kvI6Jirlk/qmFbg+lXtN65MiZg3wF81Hz1poUCf7nx4+35SpeiFMRsSmvT0XFkat42osRBarfSO7gQKIWbLjodkrc04CjW/aT7Dmhzy4KFi+U5hiIjYqlYAYnp+c07dqCkMXrU14XCirMW3M+5JOV/2H651OyXe8LuH8LDkWc/y04HHWEosULX9G6xhieH96fj0aMy6zwMl2BYoWIjzx/DMRHxVGg2MW/Bxp1ac7WxemPhXrtGrFmevYbRhtQPJCEVPknRMUSEJT2GMhdvSwBwYEcXZD2+//U1jB3Y8fpIEepouS73jXjAAAgAElEQVSpWZ4cwVe278j1K3tdhvJ91YD1l611EcaYasAQoJG1NsYYc+7o/gT4zlr7rTHmUWAs0BEYBtxlrY0wxhTw1O0NHLPW1jfG5ACWG2PmWWtDM3i/x4HHAYJuKEuhXEX/beiXyild2T/t9C1cNJDRnwxnyDNvpLtCLdnTxf4fb7+3CeVqVGB418FpygsULcjTHzzPJy98mP32gYwumF9hCuuXrqdSrUq8N/U9jsUeY8f6HbiSXNc0vKyQwddA2v9HY6j1eg/WPPvFRbeRr1IJag59gKUPjMmECDOXyWgnuMg+0NhzDLzRdUhKWVxUDC+3fo6CRQsy4KtBrJ79F8dijmVStJkkw33gyla9v9e9LF+4kkOpLoZlOxl+D2T8AdzS8XZurFmO97oOT1Oer0gBStxUmq3ZbfgYXP570BjKvPYIe5/7OF21wxMXkqtiSWrMeYez4Uf4e+0OrCv7fQ/+a5oDI1nNGPMp0Bj3PJiBV7BKM+BXa20MgLU2zlN+K3Cf5/n3uOfUACwHxhtjJgHnZoS2AmoaYzp7XucHKgLpGjDW2i+BLwGqF2uYKWeFh6IOUzz4fMOoWHBRjkQfucQaaeXJm5txP77Px2O+YNO6/6NJez4kLjqWwKDzV8sKBQUSdyguXb0ajWpxX//7Gd5lSMqQGYBceXMx6JtXmfDuD+zesCvdete7mKiYNL0mhYMKE3so9hJrpDXx44lM/HgiAC99/BKRoZHXPMbMdioqjtwlzl9tzh1UKGVYGIBf3pzkr1yKplOGApCzSH4ajX+B5b3eIz4klFxBhbjtv8+z+pnPObk/+53EXngMBAYFEp/BMVC9UU069u/MG12GpjkGzok/HE/4roPc1KAqq2evyNSYr7XDUUcoVuL8b0HRoCIcORRziTXOq1GvGnVuqUXnXh3JnScXfv7+nDp5mk9GXbzBe705Gh1HweDzx0DBoEIcPZx+H6jcqAZt+t/He12Hp9sH6rW9jY1zV5OcDS9iJETFEpAq/4CgQBKiz+fvzJuLXJVLU3XyCAD8ixTgpvGD2NlrNCc37WX/a9+k1K02fRRn9kVlXfCSKTSE7PqyFbj53AtrbT+gOXDhmI8k0v7f5fT8a7iya7PWs/2+wFCgFLDRGBPo2cbT1trankdZa+28f5PMtbBlw3ZKlytFidJB+Pn70aZjSxbN/fOK1vXz9+Oj8W8x/ZfZzJvxRyZHKpllT8hugsoGUbRUUfz8/WjU7nbWzl+dpk6ZamV5fPSTvNX7TY7Hnr+y7Ofvx8AvB7Fk8iJWzv4rq0O/JnaF7CK4TDDFShXDz9+PJu2bsHL+yita1+FwcEMB9xypMpXLULZKWdYtTT+87noXv3EfecsWJ3epIhh/J6U6NCRy7vk8kv4+zfRqfZnd4DlmN3iO2PV7Uhov/vly0/j7F9k8+mdi12S/BizA3pDdFC8bRJFSRXH6+3Fru8asy+AY6DP6Kd7tPSrNMVCoeCD+OQIAyJMvDzfVq0zU3uzXiN22cQelypYkuJT7t6BVh+YsnXtlQ6Fe7TeCtvU6075BFz58fRyzf5mTrRovAGEheyhaJojAku59oF67RoTMX5umTqlqZegx6nHG9XmLv2OPp9tG/faNWD0j+w0fAzixcQ85ywaRo1RRjL8fgR0aE5/qLmquv0+xrnovNtzSlw239OXE+l0pjRdHrgAcuXIAkP+OWtgkV7rJ/z4tOTnzH16gHpjryx/AKGPMk9bazzxluTOoFwa0BTDG3AyU9ZQvBH4zxnxgrY01xhTy9ML8hftmAN8D3YFlnnXLW2tXAauMMe1wN2TmAk8aY/6w1iYaYyoBEdZar9y2yeVyMWrQu3wx8SOcTge/TZjJ3p2h9HvpMbaG7GDx3D+pXrsKH37zFvkK3EDTVo3pN/AxOjbpRuv2LajbsA4FCuanY9d7ABjyzAh2bt3tjVQyxcDhY1izYRNHjx6neccePNW7J53a3eXtsK6pZFcyXw/7kiHfvea+heykhYTvPkjXAd3Yu2kPaxespufgR8iZOxcvjHsJgJjIGN7q8ya3tm1ElQbVuKHADdzZuRkAn744lrBt6ToUr1vJrmQ+e/UzRv4wEqfTybyf53Fg1wF6vtCTXZt2sWr+KirVqsSrX71K3vx5uaXFLfQY0IO+Lfri9Hfy7mT3HepOnTjFO8+8Q7Ir+w0nsK5kNgwezx0TXsY4HYROXMLxXRFUG9iJuJBQouZdfORthUdbkbdsMao+dy9Vn7sXgKUPjOFsBid416tkVzLjh33FoO+G43A6WTxpAeG7D9J5wIOEbtrDugVr6Da4Fzlz5+RZzzEQG3mEd/uMokSFkvQY+gjWWowxzPxyGgd37vdyRv+cy+XincEf8PGE93A6HUyfOIt9u8J4YmBvtofsYOm85VStVZl3/vsm+QrcwO0tb+PxgY/StelD3g79mkh2JTNx2Nc8+90QHE4HyyctImp3OO2e78r+zXvZtGAtnQb1JEfunDw+7gUA4iJiGPfYWwAElixCwaDC7F6Z/W4hDoArmbAh/6HyT8MwTgeHJy7k9K6DlBz4ACdD9qZpzFzIPzA/lScMg2RLQnQse54em4WBS2Yx2W48uI8zxgThvo3yLcAR4CTwOXAIz22UjTG5gGlAUWAN7mFmbay1YcaYh3EPN3MBG6y1vTwT/P8LFPZs8xFr7QFjzBTcw8MM7sbPc57nI4F2nudHgI7W2ksOmM6sIWTZxYatP3k7BK/rVvd5b4fgVSeSs9ddfa61R5MuPqn8/8WUgOxze+7MsDvhyoc2+qq6OYp7OwSveiQh+w1Pu9YaRk65rm71d/rn1zP9/CxX1+FZnrN6YK4z1too3L0lGVnsqXMa91yVjNb/Fvj2grIw3PNjLqx734VluIeXDfY8RERERCS78tFJ/JoDIyIiIiIi2YZ6YEREREREfJF6YERERERERLxLPTAiIiIiIr7IqgdGRERERETEq9QDIyIiIiLiizQHRkRERERExLvUgBERERER8UXWZv7jChhjWhtjdhpj9hhjXslgeWljzCJjzAZjzCZjzN2X2p4aMCIiIiIikimMMU7gU6ANUBV40BhT9YJqQ4FJ1to6uP+g+7hLbVNzYEREREREfNH1MQemAbDHWrsPwBgzEegAbEtVxwL5PM/zA5GX2qAaMCIiIiIikllKAAdTvQ4HbrmgzmvAPGPM00AeoMWlNqghZCIiIiIivig5OdMfxpjHjTFrUz0evyAKk0FkF06eeRAYb60tCdwNfG+MuWg7RT0wIiIiIiLyr1hrvwS+vESVcKBUqtclST9ErDfQ2rO9FcaYnEBh4HBGG1QPjIiIiIiIL7LJmf+4vDVARWNMWWNMAO5J+tMvqHMAaA5gjKkC5ASOXGyDasCIiIiIiEimsNYmAf2BucB23Hcb22qMecMY095T7QXgMWNMCDAB6GXtxe/RrCFkIiIiIiI+yCZf2d9pyWzW2tnA7AvKhqV6vg1odKXbUw+MiIiIiIhkG+qBERERERHxRdfH34G55tQDIyIiIiIi2YZ6YEREREREfNGV3SUs21EDRkRERETEF10nk/ivNTVg5JqonLOot0MQL/tp3QfeDsHr2tfp5+0QvObxv1d5OwSv61m4rrdD8KpfYvd5OwSvaxtU2tsheNUrjkPeDsHrFns7gP8TasCIXAPd6j7v7RC8So0XERGR65Am8YuIiIiIiHiXemBERERERHyRemBERERERES8Sz0wIiIiIiK+yPrmXcjUAyMiIiIiItmGemBERERERHyR5sCIiIiIiIh4l3pgRERERER8UbLmwIiIiIiIiHiVemBERERERHyR1RwYERERERERr1IPjIiIiIiIL9IcGBEREREREe9SD4yIiIiIiA+y+jswIiIiIiIi3qUeGBERERERX6Q5MCIiIiIiIt6lHhgREREREV/ko38HRg0YERERERFfpCFkIiIiIiIi3qUeGBERERERX+Sjt1FWA0aue7Wb3Mwjw/vgcDpZOHEeUz+bnGZ52z4daP5AS5KTkjked4xPB44lJuIIZaqW5bE3nyR33twku5KZ/Mkk/pq5zEtZ/Hu1m9ThkeGP4XA6WDhxfgb5t6f5A61wJbk4HneMcQM/TpV/X3J58p/yyS/ZMv/LGTrqfZYuX02hggWY+sPn3g4nU9RtWpe+r/XF4XQwZ8Icfhn3S5rl1W+pzhPDn6BslbKM6TeGZbPP/z8/OuhR6jevD8CEjyawdMbSLI3932re4g5Gvz0Up9PJ999O4sP3v0izPCAggM++eofatasTFxfPow8/y8EDESnLS5YMYsXaObw1aiyfjP0agJCtizlx4iQul4ukJBfN7rg3S3O6GlWa1OK+Yb1wOB2s+PkPFnw2Lc3yO3vfw60PNMOV5OJE3HF+eulz4iNiAGj/SneqNauDcTjY+ecmJr8+3gsZXJm7WjXl/fffwOlw8N9vJvD2O5+mWR4QEMD4bz7i5jo1iIuL58HuT7J/fzgAL7/Un0d6PYArOZnnn3+VefOXkCNHDhb/MZmAHDnw83MyZcosXn/jvTTb/PCDEfR6uCsFClXKsjz/jQpNatJ6eE8cTgfrJy5m2Wcz0iyv17059R9qiXUlk3DqDDMGfc2R3RGUqFWOdqP7uCsZWPzhFHbMXeuFDK5Og6b16f/6UzidDmZN+J2fPp2YZnnNW2rQ/7WnKF+lHG/0G8mSWX8CUPu2WvQf/mRKvdLlS/NGv5Esm/tXlsYv15YaMBcwxriAzYABXEB/a+1fxpgywExrbfVr8B6LgRettWuNMWHA30AycAh4yFobfbXv4SscDgd9RjzBG92HERcdy5jp77F2wWrCdx9MqRO6dR8vtx1AwpkEWvVoQ89Bvfig/zucPX2Wj5//gOiwKAoWLcTbs95n49INnDp+0osZ/TMOh4PeI55gRPfhxEXHMnr6uxnkH5oq/9YX5P9hSv5vzXov2+V/JTre3ZJundozeMS73g4lUzgcDvqN7MfgboOJiYrho5kfsWr+Kg7sPpBS53DEYd4b8B6dnuiUZt36zepTvnp5+t3VD/8Af97+9W3WLlrLqROnsjqNf8ThcPDO+69xb/uHiYyI5o+lU/h99kJ27tiTUqfnw/dz7Ogx6tZqzn2d7+G1ES/R++FnU5a/+dYQFsxP31hrd3cP4mLjsySPa8U4DPe/8Sif9niTo9GxvDh9NFvmryV6z/kGW/i2MN5pN4jEMwk07tGSDoO6M77/R5S9uRLl6t3EmNYDAXju1zeo0LAqe1Zu81Y6F+VwOBj70Zu0vvtBwsOjWLliNjNmzmP79t0pdR595EHi449RuWpjunRpz+hRQ+jW/UmqVKlIly4dqFm7GcHBxZj7+0SqVLuds2fP0qJVF06ePIWfnx9LF//GnDmLWLV6PQB1b65JgQL5vZXyFTMOw90jevF999Ecj47jsekj2LlgPUd2n98HNk/7i7U/LgTgphY3c9fQ7vzw8Nsc3hnOl+2GkuxKJm/RAjz5+yh2LVhPsiv7XJl3OBw8O/JpXuz2MkeijvD5rE9ZPu8v9l/wPThmwNt0faJLmnU3/hVCn7v6AnBDgRv4cdm3rFmyLkvj9yrNgfm/cdpaW9taWwsYBIzOgve80/N+a4HBFy40xjizIIYsf68rUaF2RaLDojh88BBJiUksn/En9VvekqbO1hWbSTiTAMDuDTsJDCoMQFRoJNFhUQDEH47jWMwx8hXKl7UJXCV3/tFp8q/XskGaOqnz37VhJ4WCAgHfyP9K1Ktdg/z5bvB2GJmmUu1KRIZFEn0gmqTEJJZMX0LDVg3T1DkcfpiwHWFYm/aHqnTF0mxetZlkVzJnT58ldFsodZvWzcrw/5W69Wqxb99+9ocdJDExkSm/zuLue1qkqdPmnhZM+PE3AKb9NocmTW9NWXZ32xbsDz3IjlQnvtnZjbUrcGT/IWIPHsaV6GL9jL+o0ap+mjq7V2wl0fM9ELZhNwWKu78HLBb/HP74+fvhF+CP08/J30eOZXkOV6JB/Trs3RtGaOgBEhMTmTRpGu3b3ZWmTvt2rfj+e3cP5OTJs2h2Z2NP+V1MmjSNhIQEwsIOsndvGA3q1wHg5El3g93f3w8/f/+U48ThcPDWmFd5ZdDIrErxXytRuzxxYYeIP3gEV6KLLTNWclPLtMfy2ROnU577587BuW+DxDMJKY0Vvxz+2Gx4Plu59k1EhEUSdSCKpMQk/pi2mEatGqWpEx1+iH3bQy/5l+eb3HMHqxat4eyZs5kdsmQyNWAuLR+Q7lKdMSanMeYbY8xmY8wGY8ydlynPZYyZaIzZZIz5Gch1kfdbClTwrHPCGPOGMWYVcKsxpq4xZokxZp0xZq4xJshT7xljzDbPtid6ypoYYzZ6HhuMMTcYY5oaY2amyuETY0wvz/MwY8wwY8wy4H5jTHljzBzPe/1pjKl8jT7Pf6xQ8UBiomJSXsdGxVDI88OckWZdW7JhcforKxVqVcQvwI9D+7NX51ah4oHEpso/LiqWwEvk39zH8hcoXLwwRyKPpLyOiYq55D6QWuj2UOo1rUeOnDnIVzAfNW+tSZHgIpkV6jUTFFyMiPColNeREdEEBRdLUyc4VR2Xy8XxYycoFFiQ3Llz8ezzT/DW6I/Tbdday5Rp41n051QefqRr5iZxDRUoVoijkbEpr49GxZK/WMGL1m/Y5U62Ld4IQNj63exasZURa75g5Oov2L40hEN7Iy66rjcFlyjOwfDIlNfhEVEEBxe/aB2Xy8WxY8cJDCxIcHAG65Zwr+twOFi7Zh5REZtYuHApq9dsAKDfU48wY+Y8oqMPZ3ZqVy1f8UIcjzq/DxyPiiNf8fT7QP2HWvLM0vdpOehBfh/+bUp5idrleWr+Wzw1dwwzh/w3W/W+ABQJKsyRqPP/T0eij1Ak6Mq+B1Nr1r4pf0z941qGdv2zyZn/8AINIUsvlzFmI5ATCAKaZVCnH4C1tobn5H6eMabSJcqfBE5Za2saY2oC6y/y3m1xD18DyANssdYOM8b4A0uADtbaI8aYrsCbwKPAK0BZa+1ZY0wBz7ovAv2stcuNMXmBM1eQ9xlrbWMAY8xCoK+1drcx5hZg3EU+h0xnMOnKLrzKfM7t9zalfI0KDOs6KE15gaIFefqD5/nkhY8uum52cvH8m1CuRgWGd03biXc+/w99Iv//O+kPAbjC/8b1S9dTqVYl3pv6Hsdij7Fj/Q5cSa5rGl5mMOYKjvuL1HllyLN89uk3KVfdU2vdoivR0YcpXKQQv03/lt279vHX8jXXLO5Mk2GuGVet17ExpWuWZ2zX1wAofGMxilcowbCG7jkA/X4Yyo4Gm9i7entmRfuvXcn/e8Z1Lr1ucnIy9eq3In/+fEz+5WuqVbuJuLijdO7UlmYtOl+j6LNeRt/na76bz5rv5lOjw23c8XRHpr7gnjsWsXEv41q+TOEKwdz7Xl/2LA4h6WxiVod8Fa78GLiYQkULUa5yWVYvyX7zfyQ99cCkd24IWWWgNfCdSf/N2Bj4HsBauwPYD1S6RPkdwA+e8k3Apgu2t8jTaMrH+SFrLuDcbO2bgOrAfE+9oUBJz7JNwI/GmB5AkqdsOfC+MeYZoIC19lz5pfwM4Gnw3Ab84nmvL3A35NIxxjxujFlrjFm778T+K3iLfy42OobCniFhAIFBhYk/FJeuXo1GtejU/37G9BlJUsL5dHPlzcXgb4Yx8d0f2b1hZ6bEmJniomNThsQBFAoKJO4i+d/X/37e6vNmuvwHffMqE979gd0bdmVJzHJtxUTFpOk1KRxUmNhDsZdYI62JH0+kf+v+DOk+BAxEhkZefiUvi4yIpkTJ8187wSWKEx11+KJ1nE4n+fLnJT7uKPXq1+L1ES8RsnUxTz7ViwEvPsljT/QESLnSHnMkjpkz5nNz3ZpZlNHVORodS4Hg81ebCwQFcvxw+nk8lRrVoFX/+/iyz9sp3wM172pA2IbdJJw6S8Kps2xfvJEydSpmWez/RER4FKVKBqe8LlkiiKioQxet43Q6yZ8/H3Fx8UREZLBuZNp1jx07zpKlf3FXq6bUqV2d8uXLsHP7cvbsWknu3LnYse36vcnJ8eg48qXqccgXVIi/Dx29aP0t01dQuVW9dOUxeyJJOH2WopVKZrDW9etI1BGKBBVNeV2keBFioq/8exDgznZN+HPO8mxxEeeaSraZ//ACNWAuwVq7AigMXDjmIqNropcqh0tfM73T02h6yFp77hvpjLX23FFmgK2eOrWttTWsta08y+4BPgXqAuuMMX7W2jFAH9xD1VZ6eoOSSPv/nfOCGM7N7HYAR1O9V21rbZUME7L2S2ttPWttvXJ5b7xEev/enpDdBJUNpmipYvj5+9Go3e2smb8qTZ2y1crxxOinGNN7JMdjz4/t9vP346UvB7Nk8iJWzF6eKfFlNnf+QRQtVTQl/7XzV6epU6ZaWR4f/SRv9X4zXf4DvxzEksmLWDlbd1vJrnaF7CK4TDDFPMdAk/ZNWDl/5RWt63A4uKGAe35QmcplKFulLOuWXv+TV9ev20T58jdS+saS+Pv7c1/ne/h99sI0debMXsiD3d13Eetwb2uWLnF/Jne3epBa1ZpSq1pTPhs3nvff/Yyvvvie3LlzkTdvHgBy585Fs2aN2b4te8yRORCylyJlilOoZBGc/k5ubncbm+envYpcsloZHhjVh6/6vM2J2OMp5fGRMVS4pSoOpwOHn5Pyt1Th0J7wrE7hiqxZu5EKFcpSpkwp/P396dKlAzNmzktTZ8bMefTseT8AnTrdw6LFy1PKu3TpQEBAAGXKlKJChbKsXrOBwoULkT+/e+5fzpw5ad7sdnbu3Mvs3xdSsnQdKlRqSIVKDTl16jSVqzbO2oT/gciQfQSWLU6BUu59oHq7huycn/ZYLlTm/DDLis1qExfmHjJcoFQRHE73z3/+EoUpXC6Io+FHyE52huykZNkSFC9VHD9/P5p1aMpf8//Z71rzDs1YOO3/bPiYD9MQskvwnPg7gVggd6pFS4HuwB+eIWKlgZ1XUL7IGFMd+KeX/XYCRYwxt1prV3iGlFUCtgOlrLWLPPNXugF5jTGB1trNwGZjzK1AZWAdUNUYkwN346U5kO5yk7X2uDEm1Bhzv7X2F0/vU01rbcg/jPmaSHYl859hXzD0u9dwOB38MWkB4bsP0nVAN/Zu2sPaBavpObgXOXPn4oVxLwMQE3mEt/q8ya1tG1OlQTXyFriBpp3dI+A+ffEjwraFeiOVfyXZlczXw75kiCf/RZMWZpD/I578XwIgJjLGk38jqjSoxg0FbuDOlPzHZqv8r8TA4WNYs2ETR48ep3nHHjzVuyedLpj4m50lu5L57NXPGPnDSJxOJ/N+nseBXQfo+UJPdm3axar5q6hUqxKvfvUqefPn5ZYWt9BjQA/6tuiL09/Ju5Pdd2c7deIU7zzzTrYY++5yuXjphdeZPPUbnE4nP37/Czu272bQ0GfZuH4Lv89eyPffTuLz/7zHupCFxMcfpXev5y65zSJFC/PDhHEAOP38mDxpOgsXZI9bSie7kvl12H956rvBOJwOVk5aTPTucO5+/n4ObN7HlgXr6DCoBwG5c/LIuOcBiI+I4avH3mHj7JVUuq06r8x9F6xl+5KNbFl4sVHM3uVyuXj2uaHMnvUTToeD8d/+zLZtu3ht+IusXRfCzJnz+e83E/l2/Fh2bFtGfPxRuvV4CoBt23bx668z2ByyiCSXi2eeHUJycjJBQcX479cf4nQ6cDgc/PrrDGbNXuDlTP+5ZFcys4eNp+d3L2OcDjZMWsKR3RHcOaATkZtC2blgPQ0ebkW5xtVJTnRx+vhJfhvgvq186Xo30fipdiQnurA2mVlDv+FU/AkvZ/TPuFzJfPTqx7zz4xgcDge//zyHsF37eeTFh9kZsou/5q/gplo3MfI/r5E3f15ubXkrvQY8zCPN3bePLl6yGEWCixCy4sIBML7vUjc1yM6MxsSnleo2yuDu+RhsrZ2V+jbKxpicwOe4ez2SgAGeRsTFynMB3wBVgY24J+o/k+o2yvWstednarvjOGGtzZvqdW1gLJAfd8PzQ2A8sMhTZoAfrLVjjDEfA3fiHoa2DejlmSPzNtAB2A0kANOtteMvjMEYUxb4DPfQMX9gorX2jUt9bp1vbP9/vSNlNFfn/8lP6z7wdgjXhfZ1+nk7BK9ZGZ89ejMyU8/C1/8d3jLTuMjrdwhWVhka1NTbIXjVYtehy1fycYvDF1xXJwQnBnXK9POzvKMnZ3nO6oG5gLU2w9sIW2vDcM9DwVp7BuiVQZ2LlZ8GHrjIdstcpDzvBa834p5Lc6F0fd7W2qcvss2XgJcuF4O1NhT3/B8RERERya70d2BERERERES8Sz0wIiIiIiK+SD0wIiIiIiIi3qUeGBERERERX2R98y5k6oEREREREZFsQz0wIiIiIiK+SHNgREREREREvEs9MCIiIiIiPsj6aA+MGjAiIiIiIr7IRxswGkImIiIiIiLZhnpgRERERER8UbJuoywiIiIiIuJV6oEREREREfFFmgMjIiIiIiLiXeqBERERERHxReqBERERERER8S71wIiIiIiI+CBr1QMj/2PvvsOjKt42jn9nNwmIFOlJ6EjvCNJECF06qIgKCAhYAFEBC4jITxAU22tBVKTZKCpILwEp0nvovYY0QhUVSTbz/pEYCKGb7CbL/bmuXLBnnnP2mU12z84+M2dFRERERMSjVIEREREREfFGWgMjIiIiIiLiWarAiIiIiIh4I1VgREREREREPMt469UJxL3y5yh3R/8hlb+7gKdT8LiZm0d5OgXxsKCK3T2dgkeNy5LB0yl4VMuTJzydgsfd5fTzdAoedW+GXJ5OweOmHZlpPJ3D5c52bZjq78+yjV/k9j5rCpmIpIhWlXt5OgWP0gBORETEPTSAERERERHxRloDIyIiImlTKekAACAASURBVCIi4lmqwIiIiIiIeKM4TyeQOlSBERERERGRdEMVGBERERERL2S1BkZERERERMSzVIEREREREfFGXlqB0QBGRERERMQbaRG/iIiIiIiIZ6kCIyIiIiLihbSIX0RERERExMNUgRERERER8UZaAyMiIiIiIuJZqsCIiIiIiHghrYERERERERHxMFVgRERERES8kdbAiIiIiIiIeJYqMCIiIiIiXsiqAiMiIiIiIuJZqsCIiIiIiHgjVWBE3COowQMsWzuLFRvm0uvFbsna/fx8+WLsB6zYMJdZwT+Sv0AgAG0fbc6CZT8n/hyN3kqZciW5O3OmJNu37vudIcNfc3e3bluVoCqMWTqGsb+PpV3Pdsnay1Uvx2dzP2P2odnUblY7SdvTA55m9KLRjF40mjot67gr5RSnx+DaBg3/iDrNH6dNx+c8nUqqqR50P5OWT2TKiu/o2OuJZO0Vq1dg3PyvWHYkmKDmSX/HeQPz8PGPI/lh6Xi+XzIO//x53ZV2ispUuwpF5o2hyIKx5OiR/DmQtW1D7l01mULTP6fQ9M/J9miTJO2OuzNRdNl35HnzeXelnKoerF+T+at/IXjddJ7p0zlZe9WalZm++Ht2hq+hScsGHsgw5dWqV50ZKyYxa/VUnu7dKVn7fTUqMXnheDaGLqdhi3qJ20uWLc63s79m2rLv+em3b2nSOn0+HpXr3sdnv33BqGVf0fb5R5K1t+zemk8Wfc5H8z9lyI9DyZ0vd2LbmxOH8N3WHxk47k13piypSBWYazDGvAE8CbiIH78+C0wBqlpro6+IXWWtrXWdY00HigCZgdzAoYSmnsCP1zhmK6CMtfbdaxyzMDDbWlvuljuXhjkcDoaNHMSTD/cgPCyCOYunsHD+EvbtOZgY83jHhzl75hy1qzaj1cNNGTikLz279Wf6z3OY/vMcAEqVLs7YHz5l5/Y9ADSp+2ji/nN/m8K8WYvc27Hb5HA46DWsFwOfHEh0eDSfzP6EtcFrObrvaGJM1PEoPuz7IY88m/QF/f7693NvuXvp1aQXvn6+jPx5JBuWbOCv83+5uxv/iR6D62vTrBFPPtKKgUM/8HQqqcLhcNDvnRd56YlXiAo/wTdzR7Ni4SoO7zuSGBN5PJJ3Xn6PJ557LNn+gz55nW8//YH1v2/krkwZiUuP34ngcJB3cC9Cnx5ITGQ0hX76hPO/reXigaNJwv6Yt4yooaOveohcL3bi7/Xb3JFtqnM4HLz17mt0bdeLiLBIfln4LYvnL+fA3kOJMeGhEbz+whC69Uz+Rj89cjgcDBzRn2cfe5HI8Ch+nD+WpQt/5+Dew4kxEccjePPFYXTu+WSSfS/8fYFBL7zN0UOh5M6bi0kLx7FqyVr+OHfezb24fQ6Hgx5Dn+V/HQZzMuIkI2d+yPpF6wjddywx5tCOg7zSoi8XL1ykScemPDWgCx/2fh+AX7+eRoaMGWjc4SFPdcFjtAbmDmKMqQm0AO6z1lYAGgLHrhV/vcFLQntba20loDvwu7W2UsLPquvsM/NagxdvVqlKeQ4fOsrRI6HExMQyY9o8GjetnySmcbP6/DR5BgBzZiykdp3qyY7T+pFmzPhlXrLtRYoWJFfunKxdvTF1OpDCSlQqQdjhMCKORhAbE8uymcuo0bhGkpio0CgO7z6MtUnfmBUsXpBta7cR54rjn7//4dDOQ1QJquLO9FOEHoPrq1qpPNmyZvF0GqmmdOVShB4+TtjRcGJjYlk84zcebJL0JTciNJIDuw5i45KeqQsXL4TTx8n63+Of73//dYF/LvzjttxTSsYKJYg5GkZMaATExPLH3GVkblDjxjsmyFC2GM6c2flz5aZUzNJ9KtxXliOHj3HsyHFiYmKZ8+tCGjatmyTm+LFw9uzcT5yXvHsrV7kMxw6FcvxoGLExscz/dRFBTR5MEhN2LIJ9uw4Qd8Xz4MjBYxw9FArAichoTkWfJnvOe9yWe0ooVqk44YfDiTwWSWxMLCtm/U61RknP/dtXb+PihYsA7N28h5wBuRLbtq3cyt9//u3WnCV1aQBzdQFAtLX2HwBrbbS1NuzfRmPMXcaY+caYHgm3zyf8G2SMWWqM+dkYs9sY84MxxtzE/b1gjNlkjNlmjCmVcKwuxpjPE/6f1xgz3RgTkvCT5OxtjClqjNlsjLk/Yb9pCfntM8aMvCyusTFmdcJ9/WSMyZyw/V1jzE5jzFZjzAcJ29oZY7Yn3N/y//Jg3oqAgDyEH49IvB0RFklAQJ4kMf6XxbhcLs6dO0/2HElfjFu2fYgZ0+YmO37rR5oxc/r8VMg8deTyz8WJsBOJt6PDo8npn/Om9j206xBVg6qSIWMGsmbPSoWaFcgdmPvGO6YxegzubLn9cxEVFpV4Oyo8mtz+N/c7LFA0P+fPnWf4mP8xfsFX9Br0LA5H+jvt+eTNRUz4pedAbEQ0PnmTPweyNKpN4RlfEPjJG/j4J7x5M4Y8r/XgxPvfuCvdVJc3IA8RxyMTb0eERZH3ivOEt8kTkJuIsEt9jgo/Qd6AW38tK1e5NL6+vhw7fDwl00t1Of1zcjL80kSVk+HR5LjOeaBB+0ZsWpo+PqhMdXFu+PEATSG7uoXAYGPMXmARMMVauyyhLTMwGfjWWvvtVfatDJQFwoCVwAPAihvcX7S19j5jTE+gP/GVmst9Ciyz1rY1xjgTcsgOYIwpmZBPV2vtFmNMWaBSQh7/AHuMMZ8BfwODgIbW2j+NMa8BfRMGSW2BUtZaa4z5dyQwGGhirT1+2bbUd5Xx3pWfql9tTHh5TOUq5bnw99/s2bU/WVyrh5vy4nMDUiBRN7na8PcmZ8BsWr6JEhVL8OGvH3L25Fl2b9qNK9aVoum5hR6DO9qNnu/X4/RxUrFaebo2eZbI45G8PXowzR5rwuzJyauz6c4VD8H5JWv5Y/YybEwM2do3w//dfoR2GcA9T7bgz2XriY2Ivvpx0qGrfSx4s38T6VVK9DlXnpy889lgBvUZlg4fr6s+AFeNrNM2iGLlizGofTo618stS38fRbmBtfY8UAV4BjgBTDHGdElongGMv8bgBWCdtTbUWhsHbAEK38RdTkv4d+M14usDoxNyc1lrzyZsz52QT0dr7ZbL4hdba89aay8AO4FCQA2gDLDSGLMF6Jyw/RxwAfjGGPMw8O/igJXAhIQqk/NqSRtjnjHGbDDGbPjzn1M30c0bCw+LJCCff+Jt/8C8REScuGaM0+kka9bMnDl9NrG91cNN+fUq08dKly2Jj9PJtpCdKZKrO0SHRyepGOQKyMXJyJM3vf/kzybT+6HevNHhDTAQdijsxjulMXoM7mxR4SfIE3jp0/U8AbmIjry5N+Mnwk+wd/t+wo6G43LFsXzBSkqUL55aqaaa2MhofC/7tN3HPxexUUmfA3Fn/sDGxABw9qf5ZCwb38+7KpXmng4tKbp4Arlf7U7W1g3J1ber+5JPBRFhUfjnu3QxBv/APERdcZ7wNpFhJ/APvNTnPAG5ibqFQendmTPx+fcf8Pl7X7Nt047USDFVnYyITjIlLGdALk5FJn/fUeGBijzaux0jug8j9mKsO1NMs2xc6v94ggYw15AwUFhqrX0L6A38uzp4JdD0OlPDLp9g7eLmqlz/7nOz8f86S/zanAduIgcDBF+2/qaMtbabtTYWqAb8ArQB5gNYa58jvmJTANhijElWq7XWfm2trWqtrXp3hhy3kPa1hWzaTpGiBSlQMB++vj60frgpwfOXJIkJnreEdo+3BqB568as/H1tYpsxhhatGzNzWvIBTJtHmjLjKtvTsr0hewksHEjeAnnx8fWhbqu6rAlec1P7OhwOstwTvzaicKnCFCldhI3L019JXY/BnW33lt3kL5KPgAL++Pj60KB1fVYsXH1T++7asocs92ThnhzZAKjyQGUO7z1yg73Sngvb9uJbKBDffHnB14cszepy/rekzwFn7uyJ/89cvwYXD8Qv2wx/ZSQH63fmYIMunBj5DedmLCL6o/HuTD/Fbdu8k8JFCpC/YCC+vj40b9OYxfPdNtPZI3Zs2UXBovnJVzAAH18fHmrTkGULbzS5I56Prw8fj3+XWT/NI3jWkhvvkAbtD9lHQJFA8iScB2q3fJD1wWuTxBQpW5TnRvRkRLdhnD159hpHEm+hKWRXkTAtK85auy9hUyXgCFCe+KlVbwJfAO66HuXihPv6v4QpZHcnbL9I/KBjgTHmvLX2x+scYw0wyhhTzFq73xiTCchP/FS3TNbaucaYNcB+AGPMvdbatcBaY0xL4gcyN/+x921yuVy8+epwfvj5KxxOJ1N+mM7e3QfoP6AXIZt3EDx/KZO/n8YnX45gxYa5nDl9lp7dX0ncv0atqoSHRXL0SGiyY7do04Sn2vdM7S6kqDhXHKPfHM2w74fhdDpZOGUhR/cepVO/Tuzdupe1wWspUbEEb455k8zZMlO9YXU69u3Icw2fw+nr5INf4q9M9df5v3i/z/vEudLfglY9Btf3ylvvsn7zVs6cOUeDNh3p2a0Tj7RscuMd0wmXK46PB33GRz++h9PhZPaUeRzae5ju/buwO2QvK4JXUapiSUaMfZss2TLzQKOadO/XhY71nyYuLo5Rb3/JJ1M+wBjDnm17mfnjHE936da54ogaOpr8Y4eBw8nZXxZycf9Rcr7QiQvb9/LnkrVk79SazPVqYF0u4s7+QcSADz2ddapxuVy8PeB9xk79DKfDyc+TZrJ/z0H6vPYs27fs4rcFyylfqQyjJr5P1mxZqdf4Qfq8+gzNH2zv6dRvm8vlYsTAjxg96WMcTie/TprNgT2H6Plqd3Zs2c2yhSsoW6k0H48bQdZ7slC3UW16vtKNh+t2pEmrBtxXoxLZsmelVftmAAx+8R327Nh3g3tNO+JccXwz+CsGfzsEh9PB4qmLOLbvGI/3fZIDW/ezftE6nhrYhYyZ7qL/F/FfkxAddoIR3d8BYNhPI8h3b34y3p2RMWvGMerVz9iyfLMnu+Q2XnIdi2RM+psHmfqMMVWAz4B7gFji39Q/A2wAqhL/Rn4ccMJa+2rC4CGzMSYI6G+tbZFwnM+BDdbaCQm3k7QnbDtMwmWUjTFVgQ+stUEJU9aqWmt7G2PyAl8DRYmvqDwPhJNwGeWENSrBwDDi18ZUtdb2Tjj+7IRjLjXG1AfeAzIk3P0gYD3x09AyEl+l+cBaO9EYMw0onrBtMfCSvc4fS/4c5e7oP6TydxfwdAriYTM3j/J0Ch4XVPHK5Xt3lnFZMtw4yIu1POnd07huxl1OP0+n4FH3Zsh14yAvN+3IzJu5eJPbRNarm+rvz/IuWXbDPhtjHgI+IX5ZwjdXu9KuMeYxYAjxq/xCrLVPXhmTGKsBjKQEDWA0gLnTaQCjAYwGMBrAaACjAYwGMMklzB7aCzQCQon/8PwJa+3Oy2KKA1OB+tba08aYPNbaqKseEE0hExERERHxTjZNjKeqAfuttQcBjDGTgdbEX2jqXz2AUdba0wDXG7yAFvGLiIiIiMhtuvyqtAk/z1wRko+kXwgfmrDtciWAEsaYlcaYNQlTzq5JFRgRERERES/kjkX81tqviV+rfS03841uPsSvvQ4i/iJTvxtjyllrz1ztgKrAiIiIiIhIagkl/mq2//r3KrhXxsyw1sZYaw8Be4gf0FyVBjAiIiIiIl7IxplU/7kJ64Hixpgixhg/4HFg5hUxvwL1AIwxuYifUnbwWgfUAEZERERERFJFwpem9wYWALuAqdbaHcaYt40xrRLCFgAnjTE7gSXAK9baa37/oNbAiIiIiIh4obTyRZbW2rnA3Cu2Db7s/xbom/BzQ6rAiIiIiIhIuqEKjIiIiIiIF7Jp43tgUpwqMCIiIiIikm6oAiMiIiIi4oXSyhqYlKYKjIiIiIiIpBuqwIiIiIiIeKGb/J6WdEcVGBERERERSTdUgRERERER8ULWejqD1KEKjIiIiIiIpBuqwIiIiIiIeCGtgREREREREfEwVWBERERERLyQt1ZgNIAREREREfFCWsQvIiIiIiLiYarAiIiIiIh4IU0hE7mO0xfOezoFj3o6Qw5Pp+BRz/yx1tMpSBqwNOQbT6fgcU0rP+/pFDzmLqefp1PwuL9c/3g6BY+KsXGeTkHuEBrAiIikgKCK3T2dgkdp8CIikvZY650VGK2BERERERGRdEMVGBERERERL+Sts/pUgRERERERkXRDFRgRERERES8UpzUwIiIiIiIinqUKjIiIiIiIF9JVyERERERERDxMFRgRERERES9k41SBERERERER8ShVYEREREREvJC1ns4gdagCIyIiIiIi6YYqMCIiIiIiXkhrYERERERERDxMFRgRERERES8Up++BERERERER8SxVYEREREREvJD10gqMBjAiIiIiIl5Il1EWERERERHxMFVgRERERES8kBbxi4iIiIiIeJgqMCIiIiIiXshbF/GrAiNpUqNGddm8ZTFbty2lX7/nk7X7+fkx8dvP2bptKUuX/UrBgvkBqF+/NitWzmLduvmsWDmLunVrJu7Trl0r1q2bz9q18/h1xkRy5szutv78F3nrVeCh39+n6aoPKdm75TXj8jWvRrvwH8hesQgAeeqUo+GCYTT+7V0aLhhG7gfKuCvl/6xBwzqs27SQjSGLeanvs8na/fz8GDvxEzaGLCZ4yc8UKJgvSXv+/AEciwihd59uidtCdixl5do5LF81k9+WT0/1PqSk6kH3M2n5RKas+I6OvZ5I1l6xegXGzf+KZUeCCWpeJ0lb3sA8fPzjSH5YOp7vl4zDP39ed6XtNoOGf0Sd5o/TpuNznk4l1dwfVJXxS79h4u/jebznY8nay1cvx+i5n7Pg0FwebFY7SVuPgd34ZtHXjP1tDL3+l/z1NL2oVa86M1ZMYtbqqTzdu1Oy9vtqVGLywvFsDF1Owxb1ErcH5Pdn0oJxTFk0gWnLvqfdU23cmfZ/8mD9mixY/QuL1v3KM326JGv38/Pl/8aMYNG6X/l5/kTyFQgAwNfXh3c/fYvZy6Ywc8kkqtWqkrhPi7ZNmL1sCrOWTmbslM/InuMed3XnP7mv7n2MXvIlXy3/mkd7PpqsvXX3Noxa/AWfLviMYZPeIXe+3IltQ779H5O2TWbw+MHuTFlSkQYw6Ygx5nwKH6+wMWZ7wv+rGmM+Tcnj3y6Hw8FHH79N2zZdqHJfI9q1a0WpUsWSxHTu8hhnzpylQvkgPv9sLEOHvQ7AyZOnefTRblSr9hDP9OjHN2M/BsDpdPL++4Np2vQJqldvyvZtu3j2uc5u79stcxjuG96F3zuMZH7dVynYpiZZSuRLFuZzd0aKd2/CyY37E7ddPPUHK576gIX1X2ddny+p/ln6eOPicDh4/6MhtHu4GzWqPsQj7VpQ8orff6fO7Th75ixVKjZg9KjxDBn6apL2d957g0XBy5Mdu2WzjtSp1Yr6ddqmah9SksPhoN87L9Kv4+t0qNeVhm3qU7h4oSQxkccjeefl9wj+dXGy/Qd98jo/jp5Ch6Cu9Gjek9PRZ9yVutu0adaILz8a5uk0Uo3D4eCFYb0Y+NQgutXvQb3W9ShYvGCSmKjjJxjZ90N++3VJku1lqpShbNWyPNP4OXo0fJaSFUtQsUYFd6afIhwOBwNH9Kfnk/1oW+dJHmrbkKIlCieJiTgewZsvDmPe9OAk209ERvNUy2dp37ALHZr2oOsLncidN5cbs789DoeDIe++TvfH+9D0gUdp0bYJxUoUSRLzaIc2nDtzjobV2jD+yx94ZXAfAB7rFP8a16Jue7q068mAt1/GGIPT6WTQO/3p1PZZWgY9zp4d++jYLfmAOK1xOBw8N+x5hnR+i14NelKnVV0KFC+QJObgjgP0bf4yfZq8wMo5K+g6sGti27SvpvHRyx+5O+00wdrU//EEDWAEAGvtBmttH0/nAVC1aiUOHjjC4cPHiImJ4eefZ9GiReMkMS2aN+aH738BYPr0uQQF1QIgJGQHEeFRAOzcuZcMGTLg5+eHMQaMIVOmTABkyZqF8PBIN/bq9uSofC/nD0fy59ET2BgXx2asIV+TKsniyr72KHtGzcb1z8XEbWe2H+FCZPyb1XN7QnFk8MXhl/ZnjVapWpGDB49wJOH3P+3nOTRr3jBJTNPmDZn0Q3wVZcb0+dQNulRpa9aiIUcOHWP3rn1uzTu1lK5citDDxwk7Gk5sTCyLZ/zGg01qJYmJCI3kwK6D2Li4JNsLFy+E08fJ+t83AvD3Xxf458I/bsvdXapWKk+2rFk8nUaqKVmpJGGHwwg/GkFsTCxLZy7lgcY1k8REhkZyaPch4mzSvwFrLX4Z/PDx88HXzxenrw+no0+7M/0UUa5yGY4dCuX40TBiY2KZ/+sigpo8mCQm7FgE+3YdIO6K50FsTCwxF2MA8Mvgi8Okjyk1Fe4ry5HDxzh25DgxMbHM+XUhDZoGJYlp2LQu06bMBmD+rMXUfLAaAMVKFmXV8nUAnIo+zbmzf1C+UhmMMRhjuCtTRgAyZ7mbqIgT7uvUbSpeqQThh8OJPBpJbEwsy2ctp3rjGklitq3elvj6tmfzHnIGXBqkbl0Zwt/n/3ZrzpK6NIBJh4wxQcaYpcaYn40xu40xPxgT/4psjHnXGLPTGLPVGPNBwrYJxphHL9s/WSUn4ZizE/4/xBgzLuE+Dhpj3DqwCQzMS+jxsMTbx4+HExCY95oxLpeLc+f+SDYlrE2bpmwN2cHFixeJjY3lpRcHsW79fA4cXEepUsWYOGFK6nfmP7rLPwd/HT+ZePuv8FPc5Z+0n/eUK0SmwJyEL9p8zePka16NM9uPEHcxNtVyTSkBgXk5HhqeeDvseMRVf///xrhcLs6dPU+OnNnJlOkuXnz5Wd4b8Vmy41prmTZjAkt+/5XOXdunbidSUG7/XESFRSXejgqPJrd/7uvscUmBovk5f+48w8f8j/ELvqLXoGdxOPSyn97k8s9JVNilN5knwqPJ6X9zFYRdm3axZXUIUzdMYurGSWxYtpGj+4+lVqqpJk9AbiLCLn3oFBV+grwBN/c8gPiplD/99i0LNv7K+FHfcyIyOjXSTFH+AXkIP36pzxFhkcn6nNc/NxEJMS6Xi/PnzpM9xz3s3r6Xhk2DcDqd5C8YSLmKpQnIl5fY2FjeenUEc5ZPYeX2BRQrWZSffpjh1n7djpz+OYm+7DlwMjyanHlzXjO+UfvGbFyy0R2ppXlx1qT6jyfoTJZ+VQZeAsoARYEHjDE5gLZAWWttBeC/zKkoBTQBqgFvGWN8/2O+N81c5dMxe2WN8gYxpUsXZ+iw13nhhYEA+Pj40KNHR2rVbM69Rauxfftu+r/SM2UTTwVX+6AwyWNhDBX/15GQIT9c8xhZS+SjwqDH2fjq2FTIMOX9l9//62+8yOhR4/nzz7+StT/UsD1BtVvT7uGn6f5MR2o9cH+K5ZyaburxuAanj5OK1crz+dAv6d7seQILBtDssSYpnaKksqv9DdzsvI3AwoEUKlaAx6t1oP39T1K5VkXKVy+Xwhmmvhu+Ft5AZFgU7eo/Rcuaj9HqsWbkyJUO1kDexHP/Wq8PP/84k4iwSKYv+o43hvVj0/oQYmNd+Pj48ESXR2ldvwMPlGvC7p37eO6lrsmOkdbcyu8/qG0QxSoUY9pXv6RyVuJJGsCkX+ustaHW2jhgC1AYOAdcAL4xxjwMJH8Xd/PmWGv/sdZGA1FAspW/xphnjDEbjDEbYmP/+A93ldTx4xHkzxeYeDtfvoDEaWH/Crssxul0kjVrFk6dip8uFZjPn0mTv6JH974cOnQUgAoV4xew/3t72i9zqFEj+VSstOav8FNkynfpU6ZMATkSp4UB+GTOSLZSBQiaNohm6/6PnPcV44EJ/RIX8t8VkINa415mXZ8v+fNIVLLjp0VhxyPIlz8g8XZgPv+r/v7/jXE6nWTNlpnTp85Q9f6K/G/oq4TsWMrzPbvQt//z9Hg2frFvRET8MaJPnGL2rGDuq5I+1gFEhZ8gT2CexNt5AnIRfZOfHp8IP8He7fsJOxqOyxXH8gUrKVG+eGqlKqnkRHg0eQIvffKeOyAXJyNPXmePS2o3qcXOzbu58NcFLvx1gXVLNlC6cunUSjXVRIadwP+ySmyegNxERdx6FeVEZDQH9hzivhqVUjK9VBERFklAvkt99g/Mm6zPEeFR+CfEOJ1OMmfNzJnTZ3G5XAx/8yNa1XuS55/qR9asWThy8Cily5UA4OjhUADmzQim8v1p/7UwOvwkuS57DuQMyMWpqFPJ4irWrshjvdszrNtQYtPBjAN3sNak+o8naACTfl0+kd0F+FhrY4mvmPwCtAHmJ7THkvC7Tphq5nc7x78ywFr7tbW2qrW2qo9Pys0/37gxhHuLFaZQofz4+vry6KMtmTMn6aLMOXOD6dDxEQDatm3GsmWrAMiWLSvTfhnPW4NHsmbNpfJxWFgEpUoXJ1euHADUb1CbPbv3k9ad3nKQzEX8yVQgN8bXSYHWNQhbcKlfsX/8zcyyzzG32kvMrfYSJzftZ2WXDzkdcgjfrJmo/V1/to2Ywsn1ez3Yi1uzaeNW7r23EAUTfv8PP9qceXOTLk6fP3cxT3SIX6Tauu1DLF+2BoBmjZ+gYtkgKpYNYvQXE/jog9GM+eo7MmW6i8yZ7wYgU6a7qF+/Nrt2po81Mru37CZ/kXwEFPDHx9eHBq3rs2Lh6pvad9eWPWS5Jwv35MgGQJUHKnN475HUTFdSwZ6QPeQrnA//Annx8fUhqFUQq4LX3NS+UWEnqFi9Ag6nA6ePkwo1ynN0/9FUzjjl7diyi4JF85OvYAA+vj481KYhyxauuKl98wTkJkPG+NNelmxZqHR/eQ7vT/vPg22bd1K4SAHyFwzE19eH5m0as3j+siQxi+cv4+H2LQB4qGUD1qxYD0DGuzImrnN5oG51XC4X+/ceIjI8imIli5Ij5z0JFVGyhAAAIABJREFUbTU4sPew+zp1m/aF7CWwSCB5E54DdVrWYV3w2iQxRcsWpdeI3gztNpSzJ896KFNxl7S/oldumjEmM5DJWjvXGLMG+Pcd+mGgCjAVaA24bTrY7XC5XPTrO5gZM7/F6XTy7bdT2bVrH4PefJlNm7Yxd84iJk6YyjdjP2LrtqWcPn2Gzk+9AMCzzz1F0XsL8fqAPrw+IH7pTquWnYgIj2L48E9YsHAqsTExHD12nGef6e/Jbt4U64pj88AJ1Jn0Gsbp4NDkZZzbe5yyrzzCqZBDhC/cdM19iz3dmMxF8lLmpbaUeSn+zf7yx9/ln5Pn3JX+bXG5XLza73/88ut4nE4nP3z3E7t37WPAoBfZsmk78+Yu5ruJU/nymw/ZGLKY06fP0K3LS9c9Zu48ufh+0hcAOH18+GXqTBYvSn6VsrTI5Yrj40Gf8dGP7+F0OJk9ZR6H9h6me/8u7A7Zy4rgVZSqWJIRY98mS7bMPNCoJt37daFj/aeJi4tj1Ntf8smUDzDGsGfbXmb+OMfTXUpxr7z1Lus3b+XMmXM0aNORnt068UhL75kqF+eK47M3R/Hu98NxOB3Mn7KQI3uP0LnfU+zdupfVwWsoWbEEQ8YMJnO2LNRsWIPOfZ+ie8NnWD7ndyrVqsiY4K/AWtYv28CaRWtvfKdpjMvlYsTAjxg96WMcTie/TprNgT2H6Plqd3Zs2c2yhSsoW6k0H48bQdZ7slC3UW16vtKNh+t2pGjxwvQb8gLWWowxTBw9if27D3q6Szfkcrn434CRjJv6OU6Hk58nzWD/noO8+NpzbNuyk98WLOenH2bwwRdDWbTuV86cPsvLz8RPm86ZKzvjpn6OjbNEhEfRv+ebAERFRvP5+1/z48xviImJJSw0nNdeGOLBXt6cOFccX775Jf/77m0cTgeLpgRzdO9ROvTtwL5t+1gXvI6ubzxNxkwZeX10/FVJT4SdYFi3oQC8+/N75L83Pxnvzsj4tRP49JVP2bz82udPb+KpNSqpzdzKHFLxLGPMeWttZmNMENDfWtsiYfvnwAZgATADyAgY4ANr7URjTN6E7Q5gMfBCwnEKA7OtteUuP6YxZghw3lr770UAtgMtrLWHr5Xb3ZkK39F/SBOyPeDpFDzqmT/S3xuilFYmW8EbB3mxpSHfeDqFNKFp5fRxufLUcCIm5aYSp1d/ubzvKn+3otRdATcO8nKzjs5OUyOGtYEPp/r7s+ph09zeZ1Vg0hFrbeaEf5cCSy/b3vuysGpX2S8SuPx6gwMSth8Gyl15TGvtkCv2T38rPkVERETucN766bLWwIiIiIiISLqhCoyIiIiIiBfy1jUwqsCIiIiIiEi6oQqMiIiIiIgX8tT3tKQ2VWBERERERCTdUAVGRERERMQLxXk6gVSiCoyIiIiIiKQbqsCIiIiIiHghi3eugdEARkRERETEC8V56TdZagqZiIiIiIikG6rAiIiIiIh4oTgvnUKmCoyIiIiIiKQbqsCIiIiIiHghb13ErwqMiIiIiIikG6rAiIiIiIh4IX2RpYiIiIiIiIepAiMiIiIi4oW0BkZERERERMTDVIEREREREfFCWgMjIiIiIiLiYarAiIiIiIh4IW+twGgAIyniYmyMp1PwqGl+f3o6BY/qlKuKp1PwuF4Zz3k6BUkD5m0e7ekUPKpOxW6eTsGj9pwJ9XQKHvXo3SU9nYLcITSAERGR/6xp5ec9nYLH3emDFxFJe3QVMhEREREREQ9TBUZERERExAvFeWcBRhUYERERERFJP1SBERERERHxQnFaAyMiIiIiIuJZqsCIiIiIiHgh6+kEUokGMCIiIiIiXshbv8hSU8hERERERCTdUAVGRERERMQLxRkt4hcREREREfEoVWBERERERLyQty7iVwVGRERERETSDVVgRERERES8kK5CJiIiIiIi4mGqwIiIiIiIeKE477wImSowIiIiIiKSfqgCIyIiIiLiheLwzhKMKjAiIiIiIpJuaAAjIiIiIuKFrBt+boYx5iFjzB5jzH5jzOvXiXvUGGONMVWvdzwNYEREREREJFUYY5zAKKApUAZ4whhT5ipxWYA+wNobHVMDGBERERERLxRnUv/nJlQD9ltrD1prLwKTgdZXiRsKjAQu3OiAGsBImtC4cRDbty9n184VvPJKr2Ttfn5+/PDDaHbtXMHKFbMoVCh/Yturr/Zm184VbN++nEaN6iZuf6F3NzZvXsyWLb/R54XuSY7Xq2dXtm9fzpYtvzFixBup17EUULFuZT78bRQfLxtNq+cfTtberHsr3l/0Ge/N/z/e+PFtcuXLDUCufLl5Z/aHjJj7Me8Hf0rDDk3cnXqKKV23Im8s/pg3l35Cw+eTv+bV69acgcEf8tq8kfT6YRDZ8+VKbGv1egcGLPyAgYs+4pG3urgx65STqXYViswbQ5EFY8nRo12y9qxtG3LvqskUmv45haZ/TrZHk/6uHXdnouiy78jz5vPuSjnF3R9UlfFLv2Hi7+N5vOdjydrLVy/H6Lmfs+DQXB5sVjtJW4+B3fhm0deM/W0Mvf6Xfh+Daxk0/CPqNH+cNh2f83QqqaZG0P1MXj6Rn1Z8T6deTyRrr1S9AhPmf8XvRxZRr3mdJG0rji5i4sIxTFw4hpHjh7kr5RTRqFFdtm5dwo4dy+nfv2eydj8/P777bhQ7dixn+fIZiefGBg0eZNWqOWzYsJBVq+YQFFQLgLvuysj06eMJCfmNTZsWMXToNWfypDkl6lag7+IP6L/0I+o+3zJZe7UODXhx/ru8MHc4z/70FnmK5QMg0z2Z6T7pDYbsGEer/3Vxc9Z3BmPMM8aYDZf9PHNFSD7g2GW3QxO2XX6MykABa+3sm7lPXYXsDmCMcQHbAAO4gN7W2lXGmMLAIWCYtfbNhNhcQDjwlbW2tzFmCHDeWvtBauXncDj49JN3aNrsCUJDw1mzei6zZy9k1659iTFPd32CM6fPUrpMbR57rBXDh79Bhw7PU7p0cdo/1pqKleoTGJiX+fMmU6bsg5QuXZynuz1JrVrNuXgxhjmzf2DuvMXs33+IunVr0bJlE+67ryEXL14kd+6cqdW1/8w4HHQd+izDO7zFyYiTvDPzfTYuWsfxfaGJMYd3HOSNFv24eOEiDTs+xJMDOvNp7w84HXWatx5+jdiLsWTIlJH3F37KxuB1nI467cEe3TrjMLR7+2lGdXyHMxEn6T9zBNuDNxCx/3hiTOjOw7zfcgAxFy5Su2MjWg/owITen1DkvhIUrVqSdx96BYCXfn6bYjXKsH/NTk9159Y5HOQd3IvQpwcSExlNoZ8+4fxva7l44GiSsD/mLSNq6OirHiLXi534e/02d2SbKhwOBy8M68VrTw7gRHg0o2Z/xqrgNRzdd+kxiDp+gpF9P+SxZx9Nsm+ZKmUoW7UszzSOf3P/f9M+pGKNCoSs2erWPqSmNs0a8eQjrRg4NNVepj3K4XDQ750XefGJV4gKP8G4uV/y+8JVHN53JDEm4ngkQ19+jw7PtU+2/z8XLtK5cQ93ppwiHA4Hn3wyjObNOxAaGs7KlbOYPTuY3bsvnRu7dGnPmTNnKVu2Du3atWTYsAF06tSL6OhTPPLI04SHR1KmTAlmzfqee++tBsD//d/XLFu2Gl9fX+bPn0TjxkEsXLjUQ728OcZhaPV2V8Z2HMG5iJP0mjmMXcGbiLrsPBAyYxXrflgMQOmG99H8zY6M7/weMf/EEPzhz+QtmR//EgU81QWPiXPDfVhrvwa+vk7I1eo0ictnjDEO4GOgy83epyowd4a/rbWVrLUVgQHAiMvaDgItLrvdDtjhzuSq3V+ZAwcOc+jQUWJiYpgydQYtWyb9BLlly8Z8991PAPzyyxzq16udsL0JU6bO4OLFixw+fIwDBw5T7f7KlCpVnHVrN/H33xdwuVws/30NrVs/BMCzzz7FyPdHcfHiRQBOnDjpxt7emmKVihNxOJyoY5G4YmJZPWsFVRtVTxKzc/V2Ll6I78v+zXvIERA/IHPFxBJ7MRYAXz9fjCN9XkqxUKVinDgSycljUbhiXGyatYryje9PErNv9Q5iEh6Dw5v3cY9//GNgsfhm8MXH1wcfP1+cPk7+OHHW7X34LzJWKEHM0TBiQiMgJpY/5i4jc4MaN71/hrLFcObMzp8rN6VilqmrZKWShB0OI/xoBLExsSyduZQHGtdMEhMZGsmh3YeIs0lP19Za/DL44ePng6+fL05fH05Hp69B/I1UrVSebFmzeDqNVFOmcilCD4cRdjSc2JhYFs34jTpNHkgSExEayYFdB4mLc8fbNfe4//5KSc6NP/00i5YtGyeJadmyMd9//zMA06bNpV69+MclJGQH4eGRAOzcuZeMGTPg5+fH339fYNmy1QDExMSwefN28ucPcGOvbk+BSsU4eSSS0wnngZBZqynduEqSmH/O/534f79MGbA2/v1xzN//cGTDHmL/iXFrzpJEKHD56DE/EHbZ7SxAOWCpMeYwUAOYeb2F/BrA3HmyApefvf8Gdl32R9IemOrOhALz+RMaeunv+PjxcPIF+ieLOZYQ43K5OHv2HDlzZidfYPJ9A/P5s2PHbmo/WIMcObJz110ZafpQfQrkDwSgRPGi1K5djZUrZrF40c9UrVLRDb28Pdn9c3AyPDrx9snwk2T3z3HN+KD2DQlZeumNao6AXLw3///4fM03zPxyWrqrvgDckzcHZ8IuDTLPhJ8kW97s14yv8Vg9di7dAsDhTfvYu3oHQ9d/xbB1X7FreQiRB45fc9+0yCdvLmLCTyTejo2Ixidv8qphlka1KTzjCwI/eQMf/4QpdMaQ57UenHj/G3elmypy+eckKuzSY3AiPJqc/rmus8cluzbtYsvqEKZumMTUjZPYsGwjR/cfu/GOkmbk9s9FVFhU4u2o8BPkvsnfP4BfBj/Gzf2SMbNGJRv4pGWBVzu/Bea9ZozL5eLcuT/ImTPp62Pbts0ICdmR+KHdv7Jly0rz5g1ZsmRlKvUg5WTNm52zl50HzoWfIlve5OfCGp0a0X/Zxzz0+pPMGvKtO1NMs9LIVcjWA8WNMUWMMX7A48DMxBytPWutzWWtLWytLQysAVpZazdc64CaQnZnuMsYswXICAQA9a9onww8boyJIH6KWRgQ6K7kjEleGfj3k5Prx1x739279/PB+6OYP28S58//ydatO4mNdQHg9HGS/Z5sPFC7JfdXrcSPP35JiZI1kx0nLTBXq7pe49Widtu6FC1fjLfbX1rTcyo8mtceeonsebLTd8wA1s1dxdno9FWB4Bq/+6up2qY2BSvcy6fthwCQq1Be/IvlY3CN+HUPvb4fxO5qWzmwbldqZeseV/T//JK1/DF7GTYmhmztm+H/bj9Cuwzgnidb8Oey9cRGRF/9OOnE1Z7n1/wjuEJg4UAKFSvA49U6ADDyxxFsqF6ObWu3p2SKkopu5hxxPW2rtSc68iSBBQP4fOpHHNh9iONHwm68o4fd/rnxUkzp0iV4550BtGjRMUmM0+nk228/Y9So8Rw6dPTKQ6Q9N/k3sOa7YNZ8F0zFVrWo/0Ibfur3pTuykxuw1sYaY3oDCwAnMM5au8MY8zawwVo78/pHSE4VmDvDv1PISgEPAd+apK9684FGwBPAlJs96OWLtuLi/rzt5I6HhpM//6XxUr58AYQllL4vj/m3guJ0OsmWLSunTp0m9HjyfcPD4vcdP2Ey1ao/RP0Gj3Dq9Bn27z+UeKzpv84DYP2GLcTFxZEr17WrGp50KuIkOQMufdKYMyAnpyNPJYsr90AF2vR+lA+6D0+cNna501GnCd17jJLVkl21MM07E3GSewIvVRzuCcjJuatUkko8UJ7GvR/m6+4jEx+DCk2qcXjzPi7+9Q8X//qHXUu3ULhycbflnhJiI6PxDcideNvHPxexUUmnPcad+QMbEz894uxP88lYNr6Pd1UqzT0dWlJ08QRyv9qdrK0bkqtvV/cln0JOhEeTJ/DSY5A7IBcnI29u6mftJrXYuXk3F/66wIW/LrBuyQZKVy6dWqlKKogKP0GewDyJt/ME5Cb6Jn//QGJs2NFwNq3eQolyxVI8x9Rw/Grnt/Coa8Y4nU6yZs3CqVNnEuL9mTr1a7p1e5mDB48k2e+LL95l//7DfP752FTuRco4F3GKbJedB7IG5LjqeeBfW2etpkyj636NyB0jjVyFDGvtXGttCWvtvdbadxK2Db7a4MVaG3S96gtoAHPHsdauBnIBuS/bdhHYCPQDfrmFY31tra1qra3qcNx92zmt37CFYsWKULhwAXx9fWn/WGtmz16YJGb27IV06hR/9aVHHmnOkqUrE7e3f6w1fn5+FC5cgGLFirBu/WaAxMX5BQoE0qZNUyZP+RWAmTMXJM4TLl68KH5+fkRHJx8UpAUHQvbhXySA3AXy4PT1oWbL2mwMXpckpnDZInQf0ZMPug3n3MlL1ZUc/jnxzeAHwN1Z76Zk1VKEH0j7nzpe6WjIAXIX9idH/tw4fZ3c17IW24KTvq7lL1uYx4d3Z0z3kZw/eS5x++mwaIpVL4PD6cDh4+Te6qWJ3B965V2kaRe27cW3UCC++fKCrw9ZmtXl/G9rksQ4c1+aMpK5fg0uHoifIhX+ykgO1u/MwQZdODHyG87NWET0R+PdmX6K2BOyh3yF8+FfIC8+vj4EtQpiVfCaG+8IRIWdoGL1CjicDpw+TirUKM/R/engE2dJtGvLbgoUyUdAAX98fH1o2Lo+vy9cdVP7ZsmWGV8/XwCyZc9KhfvLcWjvkRvslTZs2BCS5NzYrl1LZs8OThIze3YwHTvGX7ji4YebsXRp/OOSLVtWpk+fwJtvvsfq1UlfL4cM6U/WrFno33+IW/qREkJDDpCrsD/ZE84DFVvWZFfwxiQxOQtfmnpesn5log9HuDvNNCnODT+eoClkdxhjTCniy3cngUyXNX0ILLPWnrzqdI1U5HK5ePGlQcyZ8yNOh4MJE6ewc+de3nqrPxs3hjB7djDjxk9mwoRP2bVzBadPn6FDx/jLSe7cuZeffp7F1pAlxLpc9HnxjcRFnFOnjCFHzuzExsTSp88bnDkT/+Z+/ITJfDPmQzZvXkzMxRie7vaSW/t7K+JccUwYPIYB376Fw+lk6dRFhO47xqN9n+DQ1v1sXLSeJwd2IWOmjLz4xasAnAw7wQfdh5OvWH46DuqKtRZjDLO/nsGxPenjxH25OFccPw8eR89vB+JwOlgzdSkR+0Jp9nI7jm47yPZFG2k9oCN+mTLS9YuXATh9PJoxPd5ny9w1lKhVjtcXfADWsmvZFrYvTmeL2V1xRA0dTf6xw8Dh5OwvC7m4/yg5X+jEhe17+XPJWrJ3ak3mejWwLhdxZ/8gYsCHns46RcW54vjszVG8+/1wHE4H86cs5MjeI3Tu9xR7t+5ldfAaSlYswZAxg8mcLQs1G9agc9+n6N7wGZbP+Z1KtSoyJvgrsJb1yzawZtENvyMtXXnlrXdZv3krZ86co0GbjvTs1olHWqbfy6ZfyeWK48NBn/J/P47E4XAwe8o8Du09TI/+XdkVsocVwasoXbEk744dSpZsmandqCbd+3WlQ/2uFC5eiNfe7UuctTiM4bvPJyW5ella5nK5eOmlN5k16zucTicTJ05h1669DB7cl40btzFnTjATJkxh3Lj/Y8eO5Zw6dYannuoNwPPPd+beewszYEAfBgzoA0CLFh3x8/Pl9df7sHv3PtasmQvAl19OZPz4yR7r582Ic8Uxc/AEnv72dYzTwYapS4nad5yGLz/K8W0H2bVoEzU7N6bYA+Vwxcby99k/+anfpasyvrriEzJkvgunrw9lGldhXKd3k1zBTNIfcyvzSCV9uuwyyhB/KbuB1to5CZdRnm2tLXdFfBeg6q1cRtnXL98d/Yf0aMD9Nw7yYrlNBk+n4HG9Mp67cZAX6/Wn91z96XbN23z1y1jfSepU7ObpFDxq88kDnk7Bo172r33jIC834vCPaeqSn1/l75jq78+eDf3e7X1WBeYOYK11XmP7YeIvW3fl9gnAhIT/D0m9zEREREREbo0GMCIiIiIiXsimqXpQytEifhERERERSTdUgRERERER8ULeujpRFRgREREREUk3VIEREREREfFCqsCIiIiIiIh4mCowIiIiIiJeyFu/pE8VGBERERERSTdUgRERERER8UJx+h4YERERERERz1IFRkRERETEC+kqZCIiIiIiIh6mCoyIiIiIiBdSBUZERERERMTDVIEREREREfFC+h4YERERERERD1MFRkRERETEC3nr98BoACMiIiIi4oW0iF9ERERERMTDVIEREREREfFCWsQvIiIiIiLiYarAiIiIiIh4oTgvrcFoACMpIoOPn6dT8Kh9F096OgWP+unkQU+n4HHzswV4OgWPust5Z78GSLzlIWM9nYLHZS/YwNMpeMy62GhPpyB3CA1gREREUkCdit08nYJHafAikvboKmQiIiIiIiIepgqMiIiIiIgX8s4VMKrAiIiIiIhIOqIKjIiIiIiIF9IaGBEREREREQ9TBUZERERExAvFGU9nkDpUgRERERERkXRDFRgRERERES8U56XXIVMFRkRERERE0g1VYEREREREvJB31l9UgRERERERkXREFRgRERERES+k74ERERERERHxMFVgRERERES8kK5CJiIiIiIi4mGqwIiIiIiIeCHvrL9oACMiIiIi4pW0iF9ERERERMTDVIEREREREfFCWsQvIiIiIiLiYarAiIiIiIh4Ie+sv6gCI2lUw0Z12LRlMSHbltC333PJ2v38/Jj47WeEbFvCkmXTKVgwHwBVqlZk1Zo5rFozh9Vr5tKyVWMA8uULYO68H9m4KZj1GxbQs2cXd3bnP6lZrxq//P4D01dNonPvDsnaK9eoyPcLx7Lm2BIaNA9K1n535kzM3TSNV995yQ3Z3r4mjYPYsX05u3eu4NVXeiVr9/Pz48cfRrN75wpWrZhFoUL5E9tee7U3u3euYMf25TRuVBeADBkysHrlbDZuCCZky2+8NbhfsmP+38dDOXNqb+p1KhU8WL8m81f/QvC66TzTp3Oy9qo1KzN98ffsDF9Dk5YNPJBh6qhVrzozVkxi1uqpPN27U7L2+2pUYvLC8WwMXU7DFvUStwfk92fSgnFMWTSBacu+p91TbdyZdoqpEXQ/k5dP5KcV39Op1xPJ2itVr8CE+V/x+5FF1GteJ0nbiqOLmLhwDBMXjmHk+GHuStmtBg3/iDrNH6dNx+Tni/Qspc+F/3I4HKxcPZuffvnGLf1ICfcHVWXCsrF8u2I8j/dqn6y9fPXyfDlvFAsPz6NO8weTtD3zRnfGLv6acUu+odfbPd2VsqSiGw5gjDEuY8wWY8wOY0yIMaavMcaR0FbVGPPpDfbvYoz5/FaSMsYMvJX4K/adYIw5lJDzJmNMzVvc/3zCv4HGmJ9vN49buL8hxpjjCfluMca8m8LHb2OMKXPZ7beNMQ1T8j5SmsPh4KOP3+bhNl2oel9j2rVrRalSxZLEdO7yGGfOnKVi+XqM+mwsQ4e9DsDOHXt48IFW1KrRnDZtOvPpp+/gdDqJdcUyYMA7VLmvEfWCHqbHs08lO2Za5HA4eG14X/p06E+7up1o0qYhRUoUThITERrJkBeHs2D6oqse47nXurNp9RY3ZHv7HA4Hn37yDi1adqR8xXq0b9+G0qWLJ4l5uusTnD59llJlavN/n45hxPA3AChdujiPPdaaCpXq07xFBz77dDgOh4N//vmHho0fo0rVRlSp2pgmjYOoXu2+xONVua8C99yTza39/K8cDgdvvfsaPR7vQ7MH2tGibRPuLVEkSUx4aASvvzCE2b8s8FCWKc/hcDBwRH96PtmPtnWe5KG2DSl65fPgeARvvjiMedODk2w/ERnNUy2fpX3DLnRo2oOuL3Qid95cbsz+v3M4HPR750X6dnydJ+p1oVGbBhQuXihJTMTxSIa+/B7Bvy5Otv8/Fy7SuXEPOjfuwatdB7krbbdq06wRX37kXYOz1DgX/qtnr67s2b3frf35LxwOB32G9WZApzd4ul4P6rcOolDxgklioo5HMbLvByz+9bck28tUKUPZqmXp0eg5ujd4hlIVS1CxZgV3pu9RcW748YSbqcD8ba2tZK0tCzQCmgFvAVhrN1hr+6RCXrc9gEnwirW2EvA68NXtHMBaG2atffRW9jHGOG8cdVUfJzzGlay1r9/mMa6lDZA4gLHWDrbWXv2dbhpRtWpFDh44wuHDx4iJieHnn2fRvEWjJDHNmzfih+9/AWD69HkEBdUC4O+/L+ByuQDImCEDNqF2GhlxgpAtOwA4f/5P9uzZT0Cgv5t6dPvKVi7NscPHOX40nNiYWBbOWEzdJrWTxISHRrB/1wHi4pIXiktVKEHOXDlYs2y9u1K+LdXur8yBA4f/n737Do+i+ho4/j27SeggPYVepYOCgqB0rCAqikpV0FexgvATlKagYC8oKCqCFbGhFKlSVEB6kd5LSCUUFZBkc94/dglJCE3JDtk9H5487Mzc2T13k52dO+feO+zcuYfk5GQmTfqBdm2vz1CmXds2fPrp1wB8++00WjRv4lt/PZMm/cCJEyfYtWsv27fv4qoG9QD4+++jAISGhhASGor6/iBcLhcvjRxE/wE564Sn9hU12L1rL3t3R5OcnMK0ybNodWPTDGWi98awecM2UjVwJs+sWa86e3fuI3rPflKSU5gxeQ7Nrs94hXX/3li2btxOamrGeqckp5B8IhmAsFyhuET8FvfFUr3e5ezbtZ/9vuPAnB9+5rrrG2coE7svju0bd5xW/2BRv24tChUs4HQYF1V2fBcCREaFc8MNzZkw/iv/VOQiuLxuVaJ37SdmTywpySnM+2EB17S5JkOZuH1x7Ni4E838XahKWK4wQsJCCA0LxR0SwsGEg36M3mSHC+pCpqqkzhHeAAAgAElEQVTxwIPAo+LVTESmAojIVSKySERW+f6vmm7X0iIyQ0Q2i8iQkytFpLOILPVlHt4XEbcvA5HHt+7zs5Rz+7Itf4jIOhHpnUXIC4FKvueo6IthhYj8IiKX+9aXF5HFIrJMRIali62ciPzhe5xXRCaJyFoR+UpEfheR+r5tf/myGr8DjUTkShFZ4HudmSIScbbXPxMR2SUixXyP64vIfN/joSIyTkTmi8gOEXk83T5dfTGuEZFPReQaoB3wiu+9q+h7zzr4yrf0/b7W+Z4zV7rXfs6XwVp3rlgvtsjIcPZFx6QtR0fHEpmpsREZWTKtjMfj4fCRPylatDAA9RvUZdnymfy+bAZPPPFs2kH8pDJloqhTpzrLl13aWQmAEuHFiYuOT1uOj0mgRPj5XT0WEXoPeZS3ho3OrvAumsiocPbu25+2vC865vTfeboyHo+Hw4ePULRoYSIjs9g3yruvy+Vi+bJZxESvZe7chSxdtgqAR3rdx5Sps4iNjScnKRlRgtjouLTl2P3xlIwo4WBE/lEiojix+0/VOz4mgZIRxc97/5KRJfj650+YuWIyH7/7GQlxidkRZrYpHl6M+P0ZjwPFz/M4ABCWK4xx09/jgynvntbwMZeu7PoufPnlwQwcODJHNXaLRRQjISYhbTkhNoFiEUXPa98NKzeyetFqvl4xkUkrJ7J8wXL2bNubXaFectQP/5xwwWNgVHWHb7/M35qbgOtUtR4wGHgx3bargE5AXeBO3wl5NaAj0NiXLfEAnXwZiJNZn05nKud7rihVramqtYCPswi3LbDO93gs8JiqXgn0BU6e1b0FjFHVBkDsGardCzioqrWBYcCV6bblA/5Q1auB34FRQAff64wDXjjH6wP0TteFLONl56xdDlyP930dIiKhIlIDeBZooap1gCdUdRHwI76MlKpuP/kEIpIbGA909L1/IcDD6V4jUVWvAMb44j2NiDwoIstFZHlyyp/nEfb5kSyukKrqeZdZvmw1DepfT9Nrb+Wpvr3IlSssrUy+fHn5/MsxPP2/Yfz5518XLeZsk8XFYj3PY8Wd3W/jt7lLiNt/6Z+k//vf+dn3TU1NpX6DNpQtX58G9etRo0ZVIiJK0uGOW3jn3XEXKXr/ySp5kPl9CkT/td5x++O5s0VX2ja6i3Z33USRYoUvYnTZ73w+H2dz21Uduf+mhxjyyHCefO5RospGXszwTDbJju/CG25sQUJCIqtX/ZE9QfvR+X4GIstFUqZyGTo2uJeO9e+hXuO61Lq6VjZHZ7Lbv52FLKscfCFggohUxjvpQWi6bbNV9QCAiHwHNAFS8DYElvk+gHmArM60Wp6h3BSggoiMAqYBs9Lt84qIDAQSgB4ikh+4Bvg63Yc9l+//xsAdvsefAi9lEUMTvA0dVPUPEVmbbpsH+Nb3uCpQE5jtex03EHOO1wdvF7JXs3jdM5mmqv8A/4hIPFASaAF8o6qJvjiTzvEcVYGdqnpyBPME4BHgTd/yd77/VwC3Z/UEqjoWb8OM/HnLX7SzqOjoGEpFRaQtR0WFExMTl6lMLKWiItgfHYvb7aZQwQIkJR3KUGbz5u0c/fso1WtUZdXKdYSEhPD5F2P4auIP/PhDzhgfEB+TQMmoU9cKSkQUP++rx7Xq16De1XXo0L09efPlISQ0lKN/H+OdF/9Vr8psFb0vhtKlTp1UlYqKOP137isTHR3j/Z0XKkhS0kGio7PYd3/GfQ8fPsKChYu4vk0zNm3aRsWK5di88TcA8ubNw6YNv3J59Yxd8y5FsfvjCY8qmbYcHlmC+NiEs+wRGOL2JxAeeareJSKKEx974VmUhLhEtm/eyRUN6zJn6ryLGWK2io9JoERkxuNAYtyB897/ZNn9e2JYuXg1VWpWInr3/nPsZZyWHd+FDRteyU03t6LN9c3JnTsXBQrk58OP3qBnj6w6sVw6EmMSKZ4u61o8vDgHYs91muPV5IbGbFy5ieNHjwOwdN4yql9xOet+X3eOPQNDzsmzXZgLzsCISAW8J+2ZGxvDgHmqWhNv5iN3um2ZT24VbyNoQrqxH1VVdWhWL5lVOVU9CNQB5uM98U4/lcbJjENrVf3DV89D6Z6jrqpWO0t8WcVwJsdV1ZOu3Pp0r1FLVducx+tnJYVTv5/cmbb9k+6xB29DVM6jHumdqyP4ydc4+fx+s2LFWipWKkfZsqUIDQ2lQ4e2TJ+WcdjO9Olz6NTZ2+687bYbWbBgMQBly5ZKG6hYunQUlatUYM/ufQCMHvMSmzdv451RH/mxNv/NhtWbKF2+FJGlIwgJDaHNrS1ZOPPX89p30CPDuKV+B9pddRdvPjea6V/PuCQbLwDLlq+mUqXylCtXmtDQUO6661amTJ2VocyUqbPo0uVOAO6442bmzf8tbf1dd91KWFgY5cqVplKl8ixdtopixYpQqFBBAHLnzk3LFteyefN2pv80l1Jl6lGpSkMqVWnI0aPHckTjBWDdqg2UK1+aUmUiCQ0N4eb2bZg7Y6HTYWW79as3UqZCKaLKeD8HN7RvxYJZ5/c5KBFRnFy5vVnYAoUKULdBLXZt252d4V50G1dvonT5KCJKhxMSGkKrW1vwy6xF57VvgUL5CQ3zXk8sVLggtRvUZOeWnFX/YJUd34VDh7xC1crXUKPatXTv+hgLFiy65BsvAJvWbCaqfBThvs9A81ubsmj24vPaNz46ntoNa+Fyu3CHuKndsDZ7tgZPF7JAdUEnpiJSHHgPeEdVNVPqshAQ7XvcPdOurUWkCHAM76Dy+4GjwA8i8oaqxvu2F1DV3UCyiISqajIwN6tywN/ACVX9VkS24+0OlSVVPSLemcnuVNWvxRt4bVVdA/wG3A18hrdrWlZ+Be4C5ol3Rq8z5R43A8VFpJGqLhaRUKCKqq4/y+ufyS68maefOJUhOpu5wPe+9+mAiBTxZWH+xPt+ZbYJKCcilVR1G9AFWHAer5PtPB4PT/UZwuQfP8HtdvHpJ1+zceNWBg7qzcqV65g+bQ4Txn/Fhx+9wZp18zh48DDduz4GQKNrGvDUUw+RnJJCamoqvZ8cxIEDB2nUqD73drqdP9ZtYtGSaQAMHfIKs2bOd7Cm5+bxeHjlmTcY9eVruN0ufpw4jR1bdvF//Xqwcc0mFs76jep1LueVcS9Q8LICXNv6Gh7sdz8dm3V1OvQL4vF4eOLJgUyf9gVul4vxE75iw4YtDB3Sl+Ur1jB16mzGfTyRCePfZtOGXzl48BD3dvZOhblhwxa++WYK69bMI8Xj4fEnniU1NZWIiJKM++hN3G4XLpeLb76ZwrTpl/T8Fefk8Xh4fsArfDRpFG6Xm2++/JFtm3fw+NP/xx+rN/LzzIXUqluddye8QsFCBWne5loe/9+D3Hzt6VOO5iQej4cRz7zOmC/fwOV2M/nLqWzfvJNe/+vJ+tWbWDDrV2rUrcYb40ZQ8LICNG3dhF79enB7085UqFyOp4Y+hqoiIkwY8yXbNu1wukoXxONJ5bWBb/PmFy/jcrmY+tVP7Nyyiwf63sfGNZv5dfYiqtWpysiPhlGgUH6atG5Ez6fuo1OL+yhXuSxPj+xDqiouET5950t2bQ28Bky/ISNZtmothw4doWX7zvTq0YU72p5Pj+xLV3Z8F+ZUqZ5URg16h5c+984y+dNXM9m9ZTfd+3Zl85otLJ69hKp1qvDch0PIX6gAjVo3pFufLvRo+SALp/1CvcZ1+XDOWFBl2fzlLJ6zxOkq+U1qgN4JRs7Vh1BEPHjHkYTizQp8Cryuqqki0gzoq6q3iHe64gl4u239DHRR1XIi0h3vzGX58A6o/0JVn/M9d0dgAN5MQzLwiKouEZGX8A4+X+kbB3NaObyNoY85laUYoKo/ich4YKqqZpgCWUTK4x3PEeGry0RVfd63/gu8jblvgYGqml9Eyvmep6aI5PPVrQqwCm83sbtVdauI/KWq+dO9Tl3gbbwNuhDgTVX94CyvPxT4K3MXMhG5FvgIiMM7tqa+qjbLXF68Ew3coqq7RKQb0A9v1mSVqnYXkcbAB3gzKh2AQSffHxFpCbzqi3MZ8LCq/iMiu3yvl+ibrOBVVW3GWVzMLmQ50eWFSp27UABbfSBnnRBmhwqFIs5dKIDlcYedu1CAy+vOde5CAWzhmpyT3c5OhcsEzv2XLtTVRSqfu1CAm7tv1iU11WGvcndl+/nZ6F2T/F7nczZgTNr0yKGqelxEKuLNdlRR1RMOh3bJsAaMNWCCnTVgrAFjDRhrwIA1YILdpdaAedgPDZgxDjRg/Dq2IQfLi7f7WCjesSMPW+PFGGOMMcYY/7MGzHlQ1T+B+k7HYYwxxhhjzPkK1DEwFzwLmTHGGGOMMcY4xTIwxhhjjDHGBCC7D4wxxhhjjDHGOMwyMMYYY4wxxgQgDdAxMNaAMcYYY4wxJgBZFzJjjDHGGGOMcZhlYIwxxhhjjAlAgdqFzDIwxhhjjDHGmBzDMjDGGGOMMcYEIBsDY4wxxhhjjDEOswyMMcYYY4wxAShVbQyMMcYYY4wxxjjKMjDGGGOMMcYEoMDMv1gGxhhjjDHGGJODWAbGGGOMMcaYAJQaoDkYy8AYY4wxxhhjcgzLwBhjjDHGGBOA1DIwxhhjjDHGGOMsy8AYY4wxxhgTgFKdDiCbWAPGXBQry1Z1OgRHvXasgNMhOOqWiDJOh+C47//Z6XQIjjrq+cfpEBy3+dA+p0Mwl4CDe+Y6HYKjul35lNMhmCBgDRhjjDHG/GeFy7R0OgTHBXvjxVx6bBYyY4wxxhhjjHGYZWCMMcYYY4wJQDYLmTHGGGOMMcY4zDIwxhhjjDHGBCCbhcwYY4wxxhiTY6haFzJjjDHGGGOMcZRlYIwxxhhjjAlANo2yMcYYY4wxxlwgEblBRDaLyDYR6Z/F9j4iskFE1orIXBEpe7bnswaMMcYYY4wxASjVDz/nIiJu4F3gRqA6cI+IVM9UbBVQX1VrA98AL5/tOa0BY4wxxhhjjMkuVwHbVHWHqp4AJgK3pi+gqvNU9ahvcQlQ6mxPaGNgjDHGGGOMCUCXyI0so4C96Zb3AVefpXwP4KezPaE1YIwxxhhjjDH/iog8CDyYbtVYVR2bvkgWu2XZshKRzkB9oOnZXtMaMMYYY4wxxgQgf8xC5musjD1LkX1A6XTLpYD9mQuJSCvgWaCpqv5ztte0MTDGGGOMMcaY7LIMqCwi5UUkDLgb+DF9ARGpB7wPtFPV+HM9oWVgjDHGGGOMCUCqzo+BUdUUEXkUmAm4gXGqul5EngeWq+qPwCtAfuBrEQHYo6rtzvSc1oAxxhhjjDHGZBtVnQ5Mz7RucLrHrS7k+awBY4wxxhhjTAA6n/u05EQ2BsYYY4wxxhiTY1gGxhhjjDHGmAB0idwH5qKzDIwxxhhjjDEmx7AMjDHGGGOMMQHIH/eBcYI1YMwlL2+TKynxzMPgcnH4mxkc/HBShu0F27emWL8epMQdAODQF1M48s0MACr/MY1/tuwCICUmgf2PDPVn6BdFjaZ1uWvwfbjcLn79ai4zx0zOsL1Vj1tofHdLUlM8/JV0hAn/G01SdCJVGtXgrkHd08qFV4zkg8feZM2sZX6uwX9XqWltbhjSBZfbxcqJ8/l1zJQM2+t3akmDrq1RTyonjh5nyoCPSNgaTVSdCrQd0dNbSGD+m9+xaeZyB2rw31zT/GqeHvYkLreb7z+fwrh3Ps2w/YqGdfnf809QuXpFnn5oCHOmzgOgao3KPPtSP/IXyIvHk8qHb01g5g9znajCBbu2RSMGvtAXt9vNpM8mM/bt8Rm2h4WF8vK7z1OzTjUOJR3miQf6E703htDQEIa99iw161QnNTWV4c++ytJFKwC45bbreejJ+1FV4uMS6PvwIA4mHXKgduendeumvPbaUNxuNx9/PJFXXx2dYXtYWBgfffQGV1xRiwMHDtKlyyPs3r2Pli2vZdiw/oSFhXLiRDLPPPMC8+cvIk+e3HzxxRgqVCiLx5PKtGlzGDRopEO1O7dWra/j5VeG4Ha7mDD+K15/7b0M28PCwvjgw9eoW68mSUmH6NblUfbsiebK+nUY9c6LAAjCiy++yZQfZ6Xt53K5+OW3H9m/P5Y77+jp1zpll4Evvs7C35ZSpPBlTP7svXPvkAPVblqPrkN64HK7mDdxDlPGfJdh+00929Hs7lakpng4knSEsf3eITE6gWJRxen9/tOIy0VIqJuZ46cz9/OZDtXCXCzWgAkSInIb8B1QTVU3OR3PeXO5KDHoEaJ7PENyXCJlJ73N3/OWcGL7ngzF/vppIfHDR5+2ux4/wZ7bH/FXtBeduFzc83wP3uw8jIOxSQz4cQRrZy8nZtu+tDJ7NuxkQdunST5+gus6t+GOAV344NE32LJ4PcNv6gdA3kL5Gb5gFBsWrnGqKv+auISbhnXn004jOBKbxAM/DmPznJUkbI1OK7Puh0Us/9x7Yl611RVcP7ATn3V7mfjN+xjbdiCpnlTyl7iMh396kS1zVpLqyTnzsrhcLp4Z0Zf/u+sJ4mLi+WLGR8yf9Qs7fA1zgNjoWAY9MZxuve7NsO/xY8cZ+Njz7Nm5j+Ili/HlrHEsmvc7fx75y8+1uDAul4uhI/vT/c5exO6P49tZn/LzjAVs27IzrUyHTu05cugIra5qz83t29Bv8OM8+cAA7upyGwC3NO1IkWKF+WjiKG5v3QWXy8XAF/pyY5M7OZh0iP8NfpzOPe5i1Ctnu3m0c1wuF2+9NZybb+7Evn0x/PbbFKZOnc2mTVvTynTv3pFDhw5To8Z13HlnW4YPH0CXLo+QmJjEHXfcT0xMHNWrV2HKlM+oWPEqAN58cywLFiwmNDSUGTO+pE2bZsyaNd+hWp6Zy+Xi9Teep90tXYiOjmXhLz8wfdocNm3allamW/e7OHToMHVqNadDh1sYNrw/3bo+xob1m7m2cTs8Hg8lw4uzZMl0pk+bi8fjAaDXI/exedM2ChTM71T1Lrr2N7Xm3jva8cywV50OJVuIy8V9wx5kRKehHIg9wPAfX2blnKVEbz31Xbhr/Q4G3tKXE8dP0Krz9dwzoCujHn2Ng/EHGXJ7f1JOpJArb25envUWK2Yv5VD8QQdr5D+Xwn1gsoONgQke9wC/4r37aY6Ru3ZVkvfEkLwvFpJTODJ9AflaNHI6LL8pX7cS8btjSdwbjyc5heVTfqNOm/oZymxZvJ7k4ycA2LlqC5eFFzntea68qSF/zF+VVi4niapbkaRdcRzcm4An2cMfU5ZQtfWVGcr889extMeheXOlJcyTj59Ia6yE5AolJx7Ha9arzt6d+4jes5+U5BRmTJ5Ds+uvzVBm/95Ytm7cTmpqxobZ7h172bPT+wWfEJdIUuJBChe9zG+x/1u1r6jB7l172bs7muTkFKZNnkXLG5tlKNPqxqZ899VUAGZMmUuja70n6JWqVmDRwqUAJCUe5MjhP6lVtzoigoiQJ29uAPIXyEd8bIL/KnWBGjSoy/btu9i5cw/Jycl8/fUU2rZtk6FM27Zt+OyzbwD47rvpNG/eGIA1a9YTExMHwIYNW8idOxdhYWEcO3acBQsWA5CcnMyqVX9QqlSEH2t1/urXr8OO7bvZtWsvycnJfPPNFG6+pXWGMjff3JrPP/sWgO+//4lmza4B4Nix42mNldy5cmX43EdGhXPDDc2ZMP4r/1TET+rXrUWhggWcDiPbVKpbmbhdMcTvjcOTnMLiKb9yZeurMpTZsPgPTvi+47au2kKRiKIAeJJTSDmRAkBoWCjiEv8Gb7KFNWCCgIjkBxoDPfA1YETEJSKjRWS9iEwVkeki0sG37UoRWSAiK0Rkpog49g0XUqIoKelOMlLiEgktWfS0cvnbNKHs5DFEvPksIeHF0tZLrjDKfP02pSe+Qb6WOa/hc1nJIhzcfyBt+WBMEpdlUf+TGt/VkvXzV522vn7bxiz78ddsiTG7FQwvwpGYU+/BkZgkCoYXPq1cg66teXzh67QecA8/DZmQtj6qbkV6zX6JXjNHMvXZcTkq+wJQIqI4sfvj0pbjYxIoGVH8gp+nZr1qhIaGsndX9LkLOyw8ogQx0afqHLs/7rQ6lwwvTqyvjMfj4a8jf1G4yGVs+mMLrW5shtvtplSZSGrWqUZEVElSUlIY8r8RTFv4Fb/9MZNKVSvw9ec/+LVeFyIyMpx9+/anLUdHxxAZWfKMZTweD0eO/EnRohk/G7fddhNr1qznxImMFy8KFSrIzTe3Yt6837KpBv9NZGQ4+6Jj0pajo2OJjAzPVKZkWhmPx8PhdPWv36Auy5bP5PdlM3jiiWfTGjQvvzyYgQNHntbYN5e2wuFFOBCTmLacFHOAIuFn/i5s3rEVa+avTFsuElGUkTPeYNSSD5jy3vdBk30B7xiY7P5xgjVggkN7YIaqbgGSROQK4HagHFAL6Ak0AhCRUGAU0EFVrwTGAS84ETTegE5blTkd+tf8Jexs2Y3d7R/m6OJVhI/om7ZtR4su7LnzcWL7vkSJAQ8RWvrSvNp4RlldKDpDGuHq9tdStnYFZo39McP6gsUvI6pqGdbnwO5jZ5JVSnzZJ7N5+7o+zBk5kesea5+2Pnr1dka3fpqx7QZxba92hOQK9Weo/1kWH4EL7hJQrERRXhg1mMFPvpAzuhOcx+dezlDmmy9+JHZ/HN/P+ZRnhz/FymVrSEnxEBISwj3dO3Bri040rnk9mzZs5aEn78u2KvxXZ6rfhZSpVq0KL7wwgEcfHZChjNvt5pNPRvHuux+zc+eezE9xSfiv9V++bDUN6l9P02tv5am+vciVK4wbbmxBQkIiq1f9kT1Bm2wjWXwZnulY1vi2ppSvVZGp758aL5oUc4D+N/Sm93UPc90dzSlYrFC2xXqpUT/8c4I1YILDPcBE3+OJvuUmwNeqmqqqscA83/aqQE1gtoisBgYCpbJ6UhF5UESWi8jyrw7tzZbAU+ISCQk/deU1pGQxUuKTMpRJPfQnmpwMwOGvZ5CrRuW0bZ4Eb9nkfbEcXbqWXNUqZkuc2eVQbBKFI09dZSocUYRDmeoPcHnjWtz46O2M7vlSWqr8pPq3XMPqmUtJTfFke7zZ4UhsEgUjTr0HBSOK8GfcmQde//HjYi7P1M0OIHHbfk4c+4cSVbL8c75kxe1PIDzdlfcSEcWJj008yx4Z5cufl3c+e5V3XhrLupXrsyPEiy52fxwRUafqHB5Z8rQ6x8bEE+4r43a7yV8wP4cOHsbj8fDioNdp1/xeHu76FAULFmD3jj1Uq1kFgD27vF3qfvphNvUa1PZTjS5cdHQMpUpFpi1HRUUQExN/xjJut5uCBQuQ5JuUICoqnEmTxtKjR2927NidYb/Ro0eybdsu3nnno2yuxb8XHR1DqahTF5yiosLTusWdKhObVsbtdlMoXf1P2rx5O0f/Pkr1GlVp2PBKbrq5Fes3/sL4T0bRtOk1fPjRG9lfGfOfJcUeoGjEqd4VRSKKcjDu9O/Cmo1r0/7RDrzWc8Rp34UAh+IPsm/LHi6/qnq2xmuynzVgApyIFAVaAB+KyC6gH9CRrK/t41u/XlXr+n5qqWqbrAqq6lhVra+q9TteVjo7wuf4us2Elo0kJKokhIZQ8Kam/D1vSYYy7uKnxnzkb9GQEzu8VxRdBfMjod6r7a7LCpLniuqnDf6/1O1as40S5SIoWqoE7tAQ6rdtzJrZGWfRKl2jHJ1ffJDRPV/izwNHTnuOBu0as3RKzuw+BrB/zQ6Klg/nstLFcYe6qdm2IZtnr8hQpki5Uye7lVvUJWlXLACXlS6Oy+09zBWKKkaxChEc2nfpjnvIyvrVGylToRRRZSIICQ3hhvatWDDr/H6fIaEhvPHxSKZ8/ROzp8w79w6XiHWrNlCufGlKlYkkNDSEm9u3Ye6MBRnKzJ2xgNs73gLADW1bsuRX7+x6ufPkThvn0rjp1Xg8HrZt2UlcTDyVqlagiG8MUOOmDdmebiKES83y5WuoVKk85cqVJjQ0lDvvbMvUqbMzlJk6dTadO3cA4Pbbb2L+/EWAt3vY99+PZ9Cgl1i8OOPxYujQvhQsWIC+fYf6pR7/1ooVa6lYqRxly5YiNDSUDh3aMn3anAxlpk+fQ6fOdwBw2203po3vKVu2FG63G4DSpaOoXKUCe3bvY+iQV6ha+RpqVLuW7l0fY8GCRfTs0du/FTP/yvY1WwkvH0Hx0t7vwkZtm7BidsYZNcvWKE+PEQ/zWo8XOXLgcNr6IuFFCc0VBkC+gvmoUr8aMdsv/a60F0uqarb/OMFmIQt8HYBPVPX/Tq4QkQVAInCHiEwAigPNgC+AzUBxEWmkqot9XcqqqKozl249qSQMH02pD18Al4sj383ixLbdFH2sC8f/2Mrf85ZQuPOt5GvREFI8eA7/SeyA1wAIq1Caks89DqkKLiHpg0k5rgGT6kll4uCPeOKTZ3G5Xfw2aR4xW/fRtndHdq/bzto5y7ljQBdy5c3Ng6OfAiApOpHRD7wEQNFSxSkcUYytSzY4WY3/JNWTyvTB4+nyydOI28WqSQtI2BpN8z53sH/tTjbPWclV3dpQoUlNUpM9HDvyN9/38U4jWqZ+VZr0aktqsgfVVKYN/JijBy/tGbgy83g8jHjmdcZ8+QYut5vJX05l++ad9PpfT9av3sSCWb9So2413hg3goKXFaBp6yb06teD25t25vp2LbmiYV0KFS5Iu443ATD4iRfYvH7rOV7VWR6Ph+cGvMy4Se/gdrn55ssf2LZ5B088/RDrVm/g55kL+frzH3h19DDmLJ3MoYOH6f3gMwAULVaYcZPeQVOV2Jh4+vYaBEB8XCLvvDKWL378kOTkFPbvi+Hpx4Y6WMuz83g8PPnkIKZM+RS3282ECV+xceMWBg/uw4oV65g2bTbjx3/FuHFvsn79QpKSDtG166MAPPxwNypWLMeAAY8zYMDjANxyS2fCwkLp3/9xNm3aypIl0wF47wfPa8cAACAASURBVL0JfPzxxDPG4RSPx8NTfYYw+cdPcLtdfPrJ12zcuJWBg3qzcuU6pk+bw4TxX/HhR2+wZt08Dh48TPeujwHQ6JoGPPXUQySnpJCamkrvJwdx4EBgj3noN2Qky1at5dChI7Rs35lePbpwR9vrnQ7rokn1pDJ+8Af0/2QILreL+ZPmEr11Lx363MOOtdtYOWcZnZ7pRu68uXl8tHf2zQP7E3it5wgiK5Wi88DuqCoiwrSxk9m7OWedC5jTSY7oD23+NRGZD4xU1Rnp1j0OVMObbbkO2ALkAl5X1dkiUhd4GyiEt5H7pqp+cLbX2VLthqD+Q3rtWODO/nI+IjTM6RAc9/0/O89dKIAd9fzjdAiO2/tnzsruXWwhLrfTITju4J6ccZ+l7NTtyqecDsFRX+z+/pKa5uzaqJbZfn72S/Rcv9fZMjABTlWbZbHubfDOTqaqf/m6mS0F1vm2r8bbsDHGGGOMMeaSYg2Y4DZVRC4DwoBhvsH8xhhjjDEmADg1zXF2swZMEMsqO2OMMcYYY8ylzBowxhhjjDHGBKBAzcDYNMrGGGOMMcaYHMMyMMYYY4wxxgSgQJ1t2DIwxhhjjDHGmBzDMjDGGGOMMcYEIBsDY4wxxhhjjDEOswyMMcYYY4wxAUgtA2OMMcYYY4wxzrIMjDHGGGOMMQHIZiEzxhhjjDHGGIdZBsYYY4wxxpgAZLOQGWOMMcYYY4zDLANjjDHGGGNMALIxMMYYY4wxxhjjMMvAGGOMMcYYE4ACdQyMNWCMMcYYY4wJQHYjS2OMMcYYY4xxmGVgjDHGGGOMCUCpNojfGGOMMcYYY5xlGRhzUTz1d3D/KT2b4nE6BEf1d8U5HYLjKuYq5nQIjkrWVKdDcFyHfFWdDsFRS1MSnQ7BXAImrHjN6RBMOoE6Bia4zzqNMcYYYy6Sblc+5XQIjrLGi/EXa8AYY4wxxhgTgGwMjDHGGGOMMcY4zDIwxhhjjDHGBKBAHQNjGRhjjDHGGGNMjmEZGGOMMcYYYwKQjYExxhhjjDHGGIdZBsYYY4wxxpgAZGNgjDHGGGOMMcZhloExxhhjjDEmANkYGGOMMcYYY4xxmGVgjDHGGGOMCUA2BsYYY4wxxhhjHGYZGGOMMcYYYwKQaqrTIWQLy8AYY4wxxhhjcgzLwBhjjDHGGBOAUgN0DIw1YIwxxhhjjAlAatMoG2OMMcYYY4yzLANjjDHGGGNMAArULmSWgTHGGGOMMcbkGJaBMcYYY4wxJgDZGBhjjDHGGGOMcZhlYMwl74qmV/DA0AdxuV3MnjiLb0Z/k2H7rT3b0+aeNnhSPBxJOsJbfd8kIToBgKGfPEfVelXZuHwDz9/3vBPh/2eFmtWj3LD7EZeL+C/nsP+d77MsV+TmRlT5oB/rbujH32u3I6EhlH/5IfLXroimKrsHf8SRxev9HP3FcVWzBjz6XC/cbhfTvvyJL96dmGF77atr8ejQXlSsVoHnHxnOgmm/AFD3mjo8OuThtHJlKpbh+UeG8+vMRX6N/7+q1/QK7h/SE5fbzZyJs/h+zLcZtrfteSut7m6NJyWVI0mHebff22mfgUEThlKlXhU2Lt/Ii/cPcyL8/yzYjwEAVZrW5pbBXXG5XSz7ah4LxkzJsP2qTi1p1KU1qampnPj7H74f8CHx26LJe1l+7h3zBKVqV2TlNwv5cch4ZypwETRoVp9HnnsYl9vF9C9nMPHdrzJsr3V1LR4Z+hAVqlVg+CMvstB3HAB48NmeXN3iKsTlYsUvK3l38Gh/h/+f1W5aj65DeuByu5g3cQ5TxnyXYftNPdvR7O5WpPo+B2P7vUNidALFoorT+/2nEZeLkFA3M8dPZ+7nMx2qRfYZ+OLrLPxtKUUKX8bkz95zOpxLRqplYMzFJiKlROQHEdkqIttF5C0RCTvHPs/4K75Lgcvl4qHhDzO02xAeadmL69o1pXTl0hnK7Fi/nT439+bx6x/jt2m/ct8z96Vt++7973i99+v+Dvvicbko/+IDbOo0nDXNnqDordeSp3Kp04vly014j5v4c8WWtHUlOrUCYG3L3my8+znKDOkOIv6K/KJxuVw8Mfwxnu7yDN2a96DFrc0pW7lMhjLx0fGM7PMycyb/nGH96kVr6Hn9Q/S8/iF6d+zH8ePHWbZghT/D/89cLhcPDPs/hnd7jidaPcK17a6jVKbPwM71O+h3Sx/63PA4i6cvouuA7mnbJo/9jrd6v+HnqC+eoD8GAOIS2j1/Hx93f5k3WvejTrtrKFEpKkOZNT8s4q0b+jPqpmdY+P4Ubh7UGYDkf5KZ/do3TH/xcydCv2hcLhePD3+UAV2e5f7mD9Di1mZZHgde7vMqczMdB6pfWZ0a9WvwQOuH6NnyQS6vU4U6jWr7M/z/TFwu7hv2IC93G0a/Vo9zTbsmRGX6Lti1fgcDb+lL/xt6s3T6Iu4Z0BWAg/EHGXJ7f565qQ+Dbn2adg/fzmUlCjtRjWzV/qbWvPf6cKfDMH5iDRiHiIgA3wGTVbUyUAXID7xwjl2DqgFTuW4VYnbFELcnjpTkFBZOWcjVbRpmKLNu8Tr+Of4PAJtXbaZoRLG0bWt/W8Oxv475NeaLKX+9ShzfFcM/e+LQ5BQO/PArha+/6rRypf93L/tHT0b/OZG2Lk+V0hz5ZS0AKQcO4zn8N/nqVPRb7BfL5XWrEr1rPzF7YkhJTuHnH+bTuE3jDGVi98WxY+NONDX1jM/T9Obr+H3esrS/lZyiUt3K3s/AXu9n4Ncpv3BV66szlPlj8TpOHPf+7rdk+gys+20tx/7OuZ+BYD8GAJSuW4kDu+M4uDceT7KHNVMWU63NlRnK/JOujmF5c6X1e08+9g+7l28m5Z9kv8Z8sZ06DsSSkpzCvB8WcE2bazKUiUs7DmS64qxKWK4wQsJCCA0LxR0SwsGEg36M/r+rVLcycbtiiN8bhyc5hcVTfuXK1hm/CzYs/iPtOLB11RaKRBQFwJOcQsqJFABCw0IRV867kHU+6tetRaGCBZwO45KjfvjnBGvAOKcFcFxVPwZQVQ/QG7hfRHqJyDsnC4rIVBFpJiIjgTwislpEPvdt6yoia0VkjYh86ltXVkTm+tbPFZEyvvXjRWSMiMwTkR0i0lRExonIRhEZn+712ojIYhFZKSJfi0h+v70rmRQNL0ri/oS05QMxiRQtWfSM5Vt3bMOKeTnrCvvZhIUX5cT+A2nLJ2IOEBZRJEOZvDXLExZZlENzMtb76Ppd3saO20Wu0iXIV7siuSKLkdMUjyhGQkx82nJCbALFI878N3AmLdo14+dMV2ZzgqLhRTkQk5i2fCAmkSLhZ65/y46tWTk/cD4DwX4MAChYsjCH0x0HjsQkUahkkdPKNezSmr4L3uCG/vcyZegn/gwx2xWLKEZCzKm/g4TYBIqd53Fgw8qNrF60mq9XTGTSyoksX7CcPdv2Zleo2aJweJEMx4GkmANnPQ4079iKNfNXpi0XiSjKyBlvMGrJB0x573sOxeesBpwxmVkDxjk1gAzfsqp6BNjDGcYmqWp/4Jiq1lXVTiJSA3gWaKGqdYAnfEXfAT5R1drA58Db6Z6mMN7GU29gCvCGL5ZaIlJXRIoBA4FWqnoFsBzoczEq/G9k1ePpTDNqNLutGZVqV+K797/NcnuOlNWFsvTVF6Hc0PvY89z404rFT5zLiZgD1JrxCmWfv58/l29CPZ7sijQbnf4mXGiX3iIlilDh8vIsXbD8IsXkT1l+CLIsed1tzahUqxKT3/8uy+05UdAfAyDLNyGr92DJp7N5tWlvZoz8khaPtfdHZI4639mVIstFUqZyGTo2uJeO9e+hXuO61Lq6VjZHd3FJlsfBrOvf+LamlK9VkanvT05blxRzgP439Kb3dQ9z3R3NKVisULbFai4tqprtP06wBoxzBLLMu51pfVZaAN+oaiKAqib51jcCvvA9/hRokm6fKer9a1sHxKnqOlVNBdYD5YCGQHXgNxFZDXQDymZZAZEHRWS5iCzf/dee8wz5wiTGHKBYZPG05aIRxUiKTzqtXJ0mdbjr0Y4M7zEsLVUeCE7EHCAs8tRVtrCIopyIPVV/d/485Lm8DNW/HUa9398j/xVVqDp+APlqVwRPKruHfsy61k+x5b6RhBTKx/EdMU5U4z9JiEmgeESJtOXi4cVJjD1wlj1O17xtU36Z8RuelJzXgDsQm5ihS1TRiGIkxZ3+GajduA4dHr2TET2HB9RnINiPAQBHYpMolO44UDCiCEfOcgV97ZTFVG9d3x+h+U1iTCLFI079HRQPL86B2NP/DrLS5IbGbFy5ieNHj3P86HGWzltG9Ssuz65Qs0VS7IEMx4EiEUU5mMVxoGbj2rR/tAOv9RyR5efgUPxB9m3Zw+VXVc/WeI3JbtaAcc56IMM3jIgUBEoDh8n4u8l9huc438ZO+jInBwCkpnt8cjnE95yzfVmeuqpaXVV7ZPmkqmNVtb6q1i+bv0xWRf6zrWu2EFk+kpKlSxISGsJ1ba9j6ezfM5SpUKMCj4x4lGE9hnH4wOFsicMpf63eRu7yEeQqXQIJDaHorU04OGtZ2nbPn0dZUbM7q65+iFVXP8RfK7ewufsI/l67HVeeMFx5cgFQ6Lo6aIqHY1v3OVWVf23zms2UKh9FeOlwQkJDaHFrMxbNvrBZxFre2oK5P+S87mMA29ZsJaJ8JCV8n4Emba9lWabPQPkaFXhoRC9G9BgecJ+BYD8GAOxbs51i5cIpXKo47lA3ddo2YuPsjN3kipYLT3tctUU9EnfF+jvMbLVpzWai0h0Hmt/alEWzF5/XvvHR8dRuWAuX24U7xE3thrXZszVndSHbvmYr4eUjKF66BO7QEBq1bcKK2csylClbozw9RjzMaz1e5Ei6z0GR8KKE5vLOD5SvYD6q1K9GzPZov8ZvnJOKZvuPE2waZefMBUaKSFdV/URE3MBrwHhgB/CQiLiAKCD9SL1kEQlV1WTfc3wvIm+o6gERKeLLwiwC7sabfekE/HoBcS0B3hWRSqq6TUTyAqVUdcu5dswOqZ5U3hv0Hs99+jwut4s5X81mz5Y9dOrTia3rtrJ09lLue/Z+cufNTf8x/QFI2J/A8B7e6WJHfvMSpSqWIne+3Hz8+3je7vc2qxauPNtLXlo8qex69kMu/2Iw4nYRP3Eux7bspVS/u/l7zfYMjZnMQosW4vIvB0OqciL2ANsee/uMZS9lHk8qbw0axSufj8TlcvHTVzPYtWU39/XtxuY1W1g0ezFV61Rl+IdDyV8oP41aN6J7n27c17InAOGlSlI8sjhrFq91uCb/TqonlQ8Hv8/gT4bicruYO2kOe7fu5e4+97J97TaWzVlK12e6kztvHvqOfhqAxP0JjOjpnQ9k+NcjiPJ9Bj5YMo53/zeK1QtXOVmlCxL0xwC878GPg8dz/yf9EbeL5ZPmE781mla9OxC9bgcb56ykUbc2VGpcE09KCscO/83XT41J2/9/v75Frvx5cIeGUL3NlYzrMpL4bTnrBDbVk8qoQe/w0ucv+o4DM9m9ZTfd+3Zl85otLJ69hKp1qvDch0PIX6gAjVo3pFufLvRo+SALp/1CvcZ1+XDOWFBl2fzlLJ6zxOkqXZBUTyrjB39A/0+G4HK7mD9pLtFb99Khzz3sWLuNlXOW0emZbuTOm5vHR/cD4MD+BF7rOYLISqXoPLA7qoqIMG3sZPZuzp5eE07qN2Qky1at5dChI7Rs35lePbpwR9vrnQ7LZBMJ1Dt05gQiUhoYDVyON+MyHegLnAA+A+oCfwAlgaGqOl9EXgLaASt942C6Af0AD7BKVbuLSDlgHFAMSADuU9U9voH6U1X1G1+Zqapa0xdL+m0tgJeAXL5QB6rqj2erS9sytwT1H9KzKWed/Trg9XcdcToExxVx53U6BEcl65lngAsWNV0FnQ7BUUtTEs9dKMCVdOdzOgRHTVjxmtMhOC60WIVLapq3YgWrZPv5WeKRLX6vs2VgHKSqe4G2Z9jc6Qz7PA08nW55AjAhU5ldeMfHZN63e6YyNc+w7WegwTkrYIwxxhhjjJ9ZA8YYY4wxxpgAlBqgPa1sEL8xxhhjjDEmx7AMjDHGGGOMMQEoUMe6WwbGGGOMMcYYk2NYBsYYY4wxxpgA5NR9WrKbNWCMMcYYY4wJQNaFzBhjjDHGGGMcZhkYY4wxxhhjApBNo2yMMcYYY4wxDrMMjDHGGGOMMQFIA3QQv2VgjDHGGGOMMTmGZWCMMcYYY4wJQDYGxhhjjDHGGGMcZhkYY4wxxhhjApDdB8YYY4wxxhhjHGYZGGOMMcYYYwKQzUJmjDHGGGOMMQ6zDIwxxhhjjDEByMbAGGOMMcYYY4zDLANjjDHGGGNMALIMjDHGGGOMMcY4zDIwxhhjjDHGBKDAzL+ABGpqyQQXEXlQVcc6HYeTgv09CPb6g70HVv/grj/YexDs9Qd7D4KFdSEzgeJBpwO4BAT7exDs9Qd7D6z+Jtjfg2CvP9h7EBSsAWOMMcYYY4zJMawBY4wxxhhjjMkxrAFjAoX1d7X3INjrD/YeWP1NsL8HwV5/sPcgKNggfmOMMcYYY0yOYRkYY4wxxhhjTI5hDRhjjDHGGGNMjmENGGOMMcYYY0yOYQ0YY3IwESkrIq18j/OISAGnYzLOEJHCIlLb6TicICJuEYkUkTInf5yOyRhjTPYJcToAY/4tEbkTmKGqf4rIQOAKYLiqrnQ4NL8QkQfw3rCrCFARKAW8B7R0Mi5/EpEqwBigpKrW9J3At1PV4Q6H5hciMh9oh/dYvhpIEJEFqtrH0cD8SEQeA4YAcUCqb7UCAd2YE5Gz/o5V9XV/xeI033GgH1CWdOc1qtrCsaD8SERKAi8Ckap6o4hUBxqp6kcOh+YXIpIXeAooo6oPiEhloKqqTnU4NJONLANjcrJBvsZLE+B6YALek9lg8QjQGDgCoKpbgRKORuR/HwADgGQAVV0L3O1oRP5VSFWPALcDH6vqlUArh2PytyfwnqzUUNVavp+Abrz4FDjHTzD5GlgJDMTbkDn5EyzGAzOBSN/yFuBJx6Lxv4+Bf4BGvuV9QFBcxApmloExOZnH9//NwBhV/UFEhjoYj7/9o6onRAQAEQnBe+U5mORV1aUn3wOfFKeCcUCIiEQAdwHPOh2MQ/YCh50Owt9U9TmnY7iEpKhqMF28yqyYqk4SkQEAqpoiIp5z7RRAKqpqRxG5B0BVj0mmLwUTeKwBY3KyaBF5H+8V55dEJBfBlVVcICLPAHlEpDXQC5jicEz+ligiFfE13ESkAxDjbEh+9TzeK6+/quoyEakAbHU4Jn/bAcwXkWl4r8ICgd+FSkTePtt2VX3cX7FcAqaISC/gezL+DSQ5F5Jf/S0iRTl1HGxIcDXqT4hIHk7VvyLp/g5MYLIbWZocy9fv9QZgnapu9V2JrqWqsxwOzS9ExAX0ANoAgvdE9kMNog+174R9LHANcBDYCXRS1d2OBmb8RkSGZLU+0DMUInIC+AOYBOzHewxIo6oTnIjLCSKyM4vVqqoV/B6MA0TkCmAUUBPv30RxoIOvS23A813AGwhUB2bh7VrdXVXnOxmXyV7WgDE5mm/8S2VV/VhEigP5VTWrL7OAJiJFgFLB8oUFaQ24Dr6uE/kAl6r+6XRc/iQiL+Pt630MmAHUAZ5U1c8cDcxkO98V9zuBjni7TX4FfKuqBx0NzDjC14W4Kt6G7GZVTXY4JL/yfR4a4q3/ElVNdDgkk82sAWNyLN+V1/p4B/BWEZFI4GtVbexwaH6R1QxUQLDNQLVQVa9zOg6niMhqVa0rIrcB7YHewDxVreNwaNlORN5U1SdFZApZjP1S1XYOhOUIEYkC7gH6AE+r6qcOh+RXIhIKPAycPBbMB94PlpN4Ebk9i9WH8fZOiPd3PE7wzUBZjoyz0H3nWEAm29kYGJOT3QbUwzv7DKq6P8jug1JIVY+ISE+8M1ANEZGgycD4zBaRvnivPv99cmUQ9X0P9f1/E/ClqiYF0djVkyfprzoahcN83YfuAVoDPwErnI3IEWPwfhZG+5a7+Nb1dCwi/+qBdwaueb7lZsASoIqIPB/oDVoRGYd32vT1ZJxK3RowAcwaMCYnO6GqKiInB+7lczogP7MZqOB+3/+PpFunQFD0fcc7eHkT3i5kvXzdKI87HJNfqOoK3/8LnI7FCSLyHHALsBGYCAxQ1WCagS+9Bpmyjj+LyBrHovG/VKCaqsZB2n1hxgBXAws51dgPVA1VtbrTQRj/sgaMyckm+WYhu8x3U8f78d4XJFicnIHqt2CdgUpVyzsdg5NUtb+IvAQcUVWPiBwFbnU6Ln8QkXWcZdrwILgXzCC8M7DV8f286Mu+Cd4B7IFe//Q8IlJRVbdD2uQewTSNcLmTjRefeKCKLyMbDN3oFotIdVXd4HQgxn9sDIzJ0Xyzj6TNwqWqsx0OyfiRiHTNar2qfuLvWJzgm4mvD947UD8YTHegFpGyZ9se6DPRBXv90xORlnhvZrgD73dBWeA+VZ131h0DhIiMBsrgvaEnwB14b+bYD5iqqs2dis0fROQ6vLcQiMU7fXIwNuKDjjVgjMmhRKQU3qkzG+O9Ev0r8ISq7nM0MD8SkVHpFnMDLYGVqtrBoZD8SkS+wjvmoauq1vTdC2GxqtZ1ODTjABEpBhwIpqnUT/LdB+zkLFybVDVo7gPiu2nj7UAT36oDQISqPnLmvQKHiGzDeyFnHafGwARVIz4YWRcyk+OIyK+q2kRE/iRjF5KTV10KOhSav30MfIF3KlWAzr51rR2LyM9U9bH0yyJSiMDv751e0N+BOtNxIAzvYO6/A/044LtZ4UggCRiG9+++GOASka6qOsPJ+PxBRFqo6s9ZzMJVUUSCZhYq31jQ7XjHvNyF935Y3zoblV/tUdUfnQ7C+Jc1YEyOo6pNfP8H04xjWSmuqh+nWx4vIk86Fs2l4ShQ2ekg/Cjo70Cd+TggIu2BqxwKx5/eAZ4BCgE/Azeq6hIRuRz4Eu99gQJdU7x1b5vFtoCfhUpEqgB3452F7gDe2Rgl0LuMZWGTiHyBtxtZ2vEvWBqwwcoaMCbH8l2BXH/y5oUikh+ooaq/OxuZ3ySKSGe8Jytw6kssaGS6B4gL752YJzkXkd8NwXuiWlpEPsd3B2pHI3KYqk4Wkf5Ox+EHIao6C8A3Ve4SAFXdFCxJOFUd4nv4fOYbGItIMEzwsQn4BWirqtsARKS3syE5Ig/ehkubdOsCvgEb7KwBY3KyMcAV6ZaPZrEukN2P9yrsG3gP1os4Na1wsEh/D5AUYHcwjQFS1dkispJTd6B+ItjuQJ2p+5AL781tg2EMSGq6x8cybQuG+qf3Lacf978BrnQgFn+6A28GZp6IzMA7nXZwtF7TUdX7nI7B+J81YExOJukHq6pqqogEzd+0qu4BguZu42ewHDjm+91XAa4QkbhguQO3T27gIN7jeXVf3/+FDsfkT+m7D6UAuwiOqaTriMgRvCeseXyP8S3ndi4s//F1l6sBFMrUkC1IELwHqvo98L3vHmjtgd5ASREZA3x/MkMX6GxCm+Bks5CZHEtEvgPm4826APQCmqtqe8eC8iMRmYD3IH3It1wYeE1VgyYLIyIrgGuBwnjvPL0cOKqqnRwNzE9894DpSKY7UKtqsDdsTRAQkVvxnri3A9IP4v4TmKiqixwJzEEiUgTvxC4dVbWF0/H4g4jMxjuhzckJXDoDnVQ1aCa0CUbWgDE5loiUAN4GWuC96jIXeFJV4x0NzE9EZJWq1jvXukAmIitV9QoReQzIo6ovB9N7ICKbgdrBNGVsZiLyMjAcbzeqGXhv6vikqn7maGDGb0SkkaoudjoO4wwRWZ156vis1pnA4nI6AGP+LVWNV9W7VbWEqpZU1XuDpfHi4/JlXYC0K29B04XOR0SkEdAJmOZbF0zvwQ680wYHszaqegS4Be/N+6rgvYGfCR4PichlJxdEpLCIjHMyIONXiSLSWUTcvp/OBNmENsEomL7oTYARkeLAA0A50v0tB1EXqteARSLyjW/5TuAFB+NxwpPAALz9vdeLSAUgKO6+7XMUWC0ic8k4fejjzoXkdycbcDcBX6pqUrDMwmXS1D7ZlRZAVQ+KSFBkYQ1gE9oEJetCZnIsEVmEdwrJFYDn5HpVDZobeIlIdbxd6ASYq6obHA7JMSLiAvL7rsYHBRHpltV6VZ3g71icIiIj8Y6DOIb3/i+XAVNV9WpHAzN+IyJrgGb6/+3dfbCnZV3H8ffnsMAuDCjylOUToICL8ugGqGMpUWOjZkmAkppWPmViZjWQY5oJjpU1opIPyCg1OtmgSSUtgwVh4tbyjKCmQGpmIgQbscrCtz/u+7BnTweYkX73xf2736+ZnXOu+7c78zk8nD3f3/W9vlfVrf36EcBFVfXktskkzYoFjEZr6j2uSR6z0vN+Otkk9JeXvYqugN1Id6nfu6rqD5oGG0iSI6pq47Jnz62q81plaqFvpby9qu5OshOwa1X9R+tcGkaSl9DtxG6zG11V59z3n9K8cKDNNFnAaLSS/D7wT1X1t62ztJDkarbe97AG2Af4UlUd1C7VsBaL2CQn0d358NvAxqo6uHG0QfR3wLy0qq7u1y+kO8A+qd2HJE/l/7aSfrRZIA0uyUHAM3E3enIcaDNNnoHRmJ0MnJrke8BddH9xVVXt2jbWMJa3RyQ5HHhlozitbJ9ke7oWovdU1V1JpvSuzHHAX/YF3NOBl7DtbdRzL8k5wH7AFWxtJS3AAmZarmfrfUgkecyUdqMnbiHJbstaCP35ds75L1ijVVW7tM7wUFJVlyVZ1zrHwN5Pd3HhlcDFSR4LTOYMTFV9LcmJwKeAr9NN5Fp+K/u8ewqwtmwnmKx+jPrvAt+mK2JDV8ROYidW2wy0KeB44LS2kTRrtpBp1Ppe1yew5NblqdxCnuQNS5YLwOHA7lX1U40iPSQkdy8TpAAAEHlJREFUWVVVW1rnmKVl7YMAewG30U8im0oLHUCSTwCvq6pvtc6iNpL8K3BkVTk6d6IcaDM97sBotJL8Ml0b2aPo2keOAj5P901sCpbuQG2huwdlMhPYAJLsTfdO2w9X1bP7v8SOBs5qm2zmntM6wEPIHsAXk2xg21HSz2sXSQP7Ol0BrwlKck5VvRj44grPNKfcgdFo9e9CrwMu7Q9yHwi8tapOaBxNA0nyGeBs4Heq6pAkq4DLpzI+NclRwLVVtalf70LXTvWFtsmGk+THVnpeVRcNnUVtJDkLOIDuTZylRey7moXSYJJcVlWHL1lvB1xdVWsbxtKMuQOjMdtcVZuTkGTHqro+yQGtQ81akvPYtn1oGxN753mPqvqLJKcAVNWWJHc/0B+aI2fStQ4uumOFZ3PNQkXAv/W/duh/aQL67/unAmuS3E7XPgbwfeADzYJpEBYwGrNvJHk43QHmC5LcCvx740xD+MMVni0WNFO7gvyOJLvTf/39jsSUWkmy9PB6Vd3T70LNvSSbWLmQn9Q0QkFVvbV1Bg2vqk4HTk9yelWd0jqPhmULmeZC30byMOD8qvp+6zyzlORngEdV1Xv79QZgT7of5n67qj7RMt+Q+tHRZwBPAq6h++dwXFVd1TTYQJKcC/wD3a4LwGuAZ1bV85uFkgaW5O9ZoZitqqmch5y0JM9Y6flUBvpMlQWMRq3vdd2bbS+wm+vZ/0k+B5xYVV/v11cAxwA7A2dX1TEt8w0lyQLd4IYNdP3vobvI866mwQaUZC/g3XSDKwq4kO4iy/9sGkwaUJIjlixXAy8AtlTVbzWKpAH1bdWLVgM/SnehsQXsHJtEq4Hm07LZ//f0j6cw+3+HxeKld0k/PvS7SXZuFWpofbvUH1XV0cC1rfO00BcqJ7bOIbVUVRuXPfpcEs9GTURVPXfpOsmjgXc2iqOBWMBozE4GDpjg7P/dli6q6rVLlnsOnKW19UleAJw7pYsMk/xWVb0zyRms3DrzugaxpCb6m9cXLQBHAD/UKI7a+wZdW7HmmAWMxmyqs/+/kORXquqDSx8meSVdO9WUvIGudW5Lks1M5wD3df3Hf2maQnpo2EhXyIfuTqwbgF9qmkiDWfZGzgJwGHBlu0QagmdgNFpTnf3fn3v4FN3XfFn/+AhgR+D5VfXtVtkkSRpSklcD29EVMbcBN1TV59qm0qy5A6Mxm+Ts//7cw1OTPAs4qH/8N1X12YaxBtUXcacCjweuAt5RVbe3TTW8JPsDbwQex7aDLDy8qrmX5LSqOrX//NiquqB1Jg2nHxl/GvByup8FAjwa+HCSDVMa6DJF7sBIGp0k59O1jVwMPAfYpap+sWmoBpJcCfwp3T+Ley/wXOFQszR3lt7Avvw2ds2/JH8M7AL8elVt6p/tSndX2p1VdXLLfJotCxiN1n3cSH8b3bmA91fV5uFTaQhJrqiqQ5esJ/nDS5KNVXXEA/9Oaf5YwExbkq8A+y8f4NJfr3B9VT2hTTINwRYyjdnX6KZufaxfn0A3Unl/4IPAixvl0uwlyW50LQMA2y1dV9UtzZINYMnUpfOSvAb4JNueA5vrr1/q7ZXkDXT/3y9+fq95Pw8paqXpk1V1dxLfnZ9z7sBotJJcXFXPWOlZkmur6qD7+rMatyQ30t39kxVerqrad9hEw0pyA1unLi0391+/BJDkd+/v9ap661BZNLwkn6Ibof/RZc9/ATi+qp7XJpmGYAGj0UpyHfBTVfVv/foxwPlVtTbJ5VV1WNuE0mwkObqqPt86hyS1kuRHgHOBO9k6SnsdsAb42ar6ZsN4mjFbyDRmvwFckuSrdO9E7wO8pr+N/iNNk2mmktxvr3tVXXZ/r8+B9wL2+0vcO43vTGDvqnpSkoOB51XV7zeOphnqC5Qjl0zkDPCZqrqwbTINwR0YjVqSHYED6b5xXe/B/WlI8vf9p6uBp9BdWhbgYOALVfX0VtmG4A6jtFWSi4DfpBveclj/7Jqq8jZ2aU65A6PRSrIT3U3sj62qX0nyhCQHVNVft86m2aqqZwIk+Tjwiqq6ul8/ie5elHm3T5JP39eL9n5rYnaqqg3JNkfCtrQKI2n2LGA0ZmfT9b0e3a+/AXwCsICZjgMXixeAqromyaH39wfmxHeAP2odQnqIuDnJfvRj9ZMcB3yrbSRJs2QBozHbr6pOSPJCgKq6M8vegtPcuy7Jh4A/o/vh5ReA69pGGsSmqrqodQjpIeJXgQ8AByb5JnAD3fcCSXPKAkZj9v0ka9j6rtt+LLkLQ5PwMuDVwOKNyxfTHeaddze2DiA9VFTV14Cf6Ae4LCzeyi5pfnmIX6OV5FjgTcBaYD3wNOAXq+ofWubSsJLsABxAV8h+qaruahxpUEmeCjyOJW9ILb8XQZpnSfYGTgN+uKqenWQtcHRVndU4mqQZsYDRKPWtYo8C/gc4im4C1aVVdXPTYBpUkh+nG5l9I91/A48GXlpVFzeMNZgk5wD7AVcAd/ePq6pe1y6VNKwkn6E7E/k7VXVIklXA5VX15MbRJM2IBYxGK8nGqjqidQ61k2Qj8KKq+lK/3h/42FT+u+gvc11bfiPXhCX556pat3S8eJIrqmoKAz2kSVpoHUB6EC5Nsq51CDW1/WLxAlBVXwa2b5hnaNcAP9Q6hNTYHUl2Z+t5yKOA29pGkjRL7sBotJJ8ke7sw43AHXQtRFVVB7fMpeEk+TDdDy3n9I9OAlZV1cvapRpOf6HnocAGlgyw8B4YTUmSw4EzgCfRFfV7AsdV1VVNg0maGQsYjVaSx670vKpuGjqL2kiyI90I1afTFbAXA++rqklMo0vyYys9d8SypiLJAt05yA10b2iFCQ7zkKbGAkajk2Q18Crg8cDVwFlV5a3LEzX1KWTS1CX5fFUd/cC/U9K88AyMxugjwFPoipdn443kk9VPIfsK8B7gfcCXkzyjaagBJLmk/7gpye1Lfm1KcnvrfNLA1id5gRcZS9PhDoxGJ8nVi+Mx+3GZG6rq8Max1MDUp5BJ6gp5YGdgC7CZrechd20aTNLMuAOjMbq3RcjWscmb9BSyJL+0wrN3tMgitVJVu1TVQlXtUFW79muLF2mOrXrg3yI95ByypE0mwJp+7btu0/MvSc5i2ylkGxvmGdpxSTZX1Z8DJHkfsLpxJmlQ/RSy5W4DbvJNLmk+2UImabScQpY1wKeBD9OdB7ulql7fNpU0rCSXAofTnYsEeDJwJbA78KqqWt8qm6TZsICRpJFJ8ogly12AvwIuAd4MUFW3tMgltZDk48Dbqurafr0W+E3gbcC5VXVoy3yS/v9ZwEganSRX09+6vZJ5v8w0yQ10X3+WfQSgqvZtFE0aXJIrlhcpi89Wek3S+HkGRtIYPad1gMZOAL5eVd8CSPJS4AXAjcBb2sWSmvhSkjOBj/frE+hGqu/IkqEvkuaHOzCS5kKSPYDv1gS+qSW5DPiJqrqlv/fm48CvAYcCT6yq45oGlAbUnwV7DVvPwl1Cdy/UZmCnqvrvhvEkzYAFjKTRSXIU8A7gFro+93OAPehGw7+kqs5vGG/mklxZVYf0n78X+E5VvaVf2zIjSZprtpBJGqP3AKcCDwM+Czy7qi5NciDwMWCuCxhguySr+hGxxwCvWPKa39c1CUn+oqqOv68zcfN+Fk6aMv+ikzRGqxZHoyb5vaq6FKCqrk/SNtkwPgZclORm4E7gHwGSPJ7u/gtpCk7uP079TJw0ORYwksboniWf37nstbnvi62qtye5EHgksH7JuZ8FurMw0txbHGJRVTe1ziJpWJ6BkTQ6Se4G7qA7sLsG+J/Fl4DVVbV9q2yShpFkE/c/Tn3XAeNIGpA7MJJGp6q2a51BUltVtQt0baTAf9AN8whwEt0Fr5LmlDswkiRptJJ8oaqOfKBnkubHQusAkiRJD8LdSU5Ksl2ShSQnAXe3DiVpdixgJEnSmL0IOB74dv/r5/tnkuaULWSSJEmSRsMdGEmSNFpJ9k9yYZJr+vXBSd7UOpek2bGAkSRJY/ZB4BTgLoCqugo4sWkiSTNlASNJksZsp6rasOzZliZJJA3CAkaSJI3ZzUn2o7/UMslxwLfaRpI0Sx7ilyRJo5VkX+ADwFOBW4EbgJOq6qamwSTNjAWMJEkavSQ7AwtVtal1FkmzZQuZJEkanSRHJrkyyX8n+TzwGIsXaRosYCRJ0hi9F3gjsDvwLuBP2saRNBQLGEmSNEYLVXVBVX2vqj4B7Nk6kKRhrGodQJIk6Qfw8CQ/d1/rqjq3QSZJA/AQvyRJGp0kZ9/Py1VVLx8sjKRBWcBIkiRJGg3PwEiSpNFKcnKSXdP5UJLLkvxk61ySZscCRpIkjdnLq+p24CeBvYCXAe9oG0nSLFnASJKkMUv/8aeBs6vqyiXPJM0hCxhJkjRmG5Ospytg/i7JLsA9jTNJmiEP8UuSpNFKsgAcCnytqv4rye7Aj1TVVY2jSZoRd2AkSdKYFbAWeF2/3hlY3S6OpFlzB0aSJI1WkjPpWsaeVVVPTLIbsL6q1jWOJmlGVrUOIEmS9CAcWVWHJ7kcoKpuTbJD61CSZscWMkmSNGZ3JdmOrpWMJHviIX5prlnASJKkMXs38ElgryRvBy4BTm8bSdIseQZGkiSNWpIDgWPo7n+5sKquaxxJ0gxZwEiSpNFKck5VvfiBnkmaH7aQSZKkMTto6aI/D3NEoyySBmABI0mSRifJKUk2AQcnuT3Jpn79n8BfNY4naYZsIZMkSaOV5PSqOqV1DknDsYCRJEmjlWQBeBGwT1W9LcmjgUdW1YbG0STNiAWMJEkarSRn0t378qyqemKS3YD1VbWucTRJM7KqdQBJkqQH4ciqOjzJ5QBVdWuSHVqHkjQ7HuKXJEljdlc/eawAkuxJtyMjaU5ZwEiSpDF7N/BJYO8kbwcuAU5rG0nSLHkGRpIkjVqSA4Fj+uVnq+q6lnkkzZZnYCRJ0tjtBCy2ka1pnEXSjNlCJkmSRivJm4GPAI8A9gDOTvKmtqkkzZItZJIkabSSXAccVlWb+/Ua4LKqemLbZJJmxR0YSZI0ZjcCq5esdwS+2iaKpCF4BkaSJI1OkjPozrx8D7g2yQX9+li6SWSS5pQtZJIkaXSSvPT+Xq+qjwyVRdKwLGAkSZIkjYYtZJIkabSSPAE4HVjLkrMwVbVvs1CSZspD/JIkaczOBs4EtgDPBD4KnNM0kaSZsoCRJEljtqaqLqRri7+pqt4CPKtxJkkzZAuZJEkas81JFoCvJHkt8E1gr8aZJM2Qh/glSdJoJVkHXAc8HHgb8DDgnVV1adNgkmbGAkaSJEnSaNhCJkmSRifJn1TV65OcR3eB5Taq6nkNYkkagAWMJEkao8VJY3/YNIWkwdlCJkmSRi3JngBV9Z3WWSTNnmOUJUnS6KTzliQ3A9cDX07ynSRvbp1N0mxZwEiSpDF6PfA0YF1V7V5VuwFHAk9L8utto0maJVvIJEnS6CS5HDi2qm5e9nxPYH1VHdYmmaRZcwdGkiSN0fbLixe49xzM9g3ySBqIBYwkSRqj7/+Ar0kaOVvIJEnS6CS5G7hjpZeA1VXlLow0pyxgJEmSJI2GLWSSJEmSRsMCRpIkSdJoWMBIkiRJGg0LGEmSJEmjYQEjSZIkaTT+F4WM9sh9FDQEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb55320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.subplots(figsize = (13, 9))\n",
    "sns.heatmap(train_corr, annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(train_corr, mask = train_corr < 1, cbar=False)\n",
    "\n",
    "#plt.savefig('diabetes_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(611, 8)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = train['Outcome'].values\n",
    "X = train.drop('Outcome', axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train_part, X_val, y_train_part, y_val = train_test_split(X, y, random_state=33, test_size=0.2,stratify=y)\n",
    "X_train_part.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train_part = ss_X.fit_transform(X_train_part)\n",
    "X_val = ss_X.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.logistic回归模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "def fit_grid_point_LR(penalty, C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集训练LR\n",
    "    LR = LogisticRegression(penalty=penalty, C=C)\n",
    "    LR.fit(X_train, y_train)\n",
    "    \n",
    "    # 在训练集和校验集上测试\n",
    "    y_train_pred = LR.predict_proba(X_train)\n",
    "    y_val_pred = LR.predict_proba(X_val)\n",
    "    logloss_val = log_loss(y_val,y_val_pred)\n",
    "    logloss_train = log_loss(y_train, y_train_pred)\n",
    "\n",
    "    print(\"logloss on test: %f and on train: %f with C = %f and penalty = %s\"%(logloss_val, logloss_train, C, penalty) )\n",
    "    return logloss_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss on test: 0.444382 and on train: 0.475841 with C = 0.200000 and penalty = l1\n",
      "logloss on test: 0.439986 and on train: 0.473780 with C = 0.200000 and penalty = l2\n",
      "logloss on test: 0.443807 and on train: 0.475348 with C = 0.220000 and penalty = l1\n",
      "logloss on test: 0.439940 and on train: 0.473641 with C = 0.220000 and penalty = l2\n",
      "logloss on test: 0.443359 and on train: 0.474971 with C = 0.240000 and penalty = l1\n",
      "logloss on test: 0.439911 and on train: 0.473532 with C = 0.240000 and penalty = l2\n",
      "logloss on test: 0.442999 and on train: 0.474674 with C = 0.260000 and penalty = l1\n",
      "logloss on test: 0.439893 and on train: 0.473445 with C = 0.260000 and penalty = l2\n",
      "logloss on test: 0.442701 and on train: 0.474436 with C = 0.280000 and penalty = l1\n",
      "logloss on test: 0.439882 and on train: 0.473375 with C = 0.280000 and penalty = l2\n",
      "logloss on test: 0.442462 and on train: 0.474242 with C = 0.300000 and penalty = l1\n",
      "logloss on test: 0.439877 and on train: 0.473316 with C = 0.300000 and penalty = l2\n",
      "logloss on test: 0.442254 and on train: 0.474083 with C = 0.320000 and penalty = l1\n",
      "logloss on test: 0.439875 and on train: 0.473268 with C = 0.320000 and penalty = l2\n",
      "logloss on test: 0.441937 and on train: 0.473838 with C = 0.360000 and penalty = l1\n",
      "logloss on test: 0.439879 and on train: 0.473192 with C = 0.360000 and penalty = l2\n",
      "logloss on test: 0.441806 and on train: 0.473742 with C = 0.380000 and penalty = l1\n",
      "logloss on test: 0.439883 and on train: 0.473162 with C = 0.380000 and penalty = l2\n",
      "logloss on test: 0.441686 and on train: 0.473660 with C = 0.400000 and penalty = l1\n",
      "logloss on test: 0.439888 and on train: 0.473137 with C = 0.400000 and penalty = l2\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.36,0.38,0.4]\n",
    "\n",
    "logloss_s = []\n",
    "for i, OneC in enumerate(Cs):\n",
    "    for j, onePenalty in enumerate(penaltys):\n",
    "        tmp = fit_grid_point_LR(onePenalty, OneC, X_train_part, y_train_part, X_val, y_val)\n",
    "        logloss_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb94940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "logloss_s1 =np.array(logloss_s).reshape(len(Cs),len(penaltys))\n",
    "x_axis = np.log10(Cs)\n",
    "for j, onePenalty in enumerate(penaltys):\n",
    "    pyplot.plot(x_axis, np.array(logloss_s1[:,j]), label = ' Test-' + onePenalty)\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'logloss' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`基本上C>100后L1与L2的选择已经很相似了，总体来说，l2比l1的模型好，损失小\n",
    "继续调整参数至0.3左右，损失不再变化，此时调参意义不大了`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best C: 0.320000 \n",
      " best penalty: l2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.32, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestCs = logloss_s1.argmin(axis = 0)\n",
    "\n",
    "best_logloss = logloss_s1[bestCs[0],0]\n",
    "best_penalty_index = 0\n",
    "best_penalty = penaltys[best_penalty_index]\n",
    "\n",
    "for j, onePenalty in enumerate(penaltys):\n",
    "    if logloss_s1[bestCs[j],j] < best_logloss:\n",
    "        best_logloss = logloss_s1[bestCs[j],j]\n",
    "        best_penalty_index = j\n",
    "        best_penalty = penaltys[best_penalty_index]\n",
    "\n",
    "bestC = Cs[bestCs[best_penalty_index]]\n",
    "\n",
    "print(\"best C: %f \\n best penalty: %s\"%(bestC, best_penalty) )\n",
    "    \n",
    "LR = LogisticRegression(penalty=best_penalty, C=bestC)\n",
    "LR.fit(X_train_part, y_train_part)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 使用SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "gammas = [0.0001,0.001, 0.01, 0.1, 1]\n",
    "#gammas =[1e-5, 1e-6]\n",
    "param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.756544502617801\n",
      "{'C': 1, 'gamma': 0.0001}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc156eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_gamms = len(gammas)\n",
    "\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_gamms)\n",
    "#train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_gamms)\n",
    "#train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(gammas):\n",
    "    pyplot.plot(x_axis, test_scores[:,i], label= gammas[i])\n",
    "    #pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = gammas[i] )\n",
    "    #pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuary' )\n",
    "#pyplot.savefig('SVMGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
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